diff --git a/.github/workflows/release-test.yml b/.github/workflows/release-test.yml deleted file mode 100644 index e28ac0e..0000000 --- a/.github/workflows/release-test.yml +++ /dev/null @@ -1,29 +0,0 @@ -name: Release to Test.PyPI -on: - push: - branches: - - '!main' - - '!master' -jobs: - build-and-publish: - runs-on: ubuntu-20.04 - steps: - - uses: actions/checkout@v3 - - name: Install Poetry - run: pipx install poetry - - name: Set up Python - uses: actions/setup-python@v4 - with: - python-version: 3.8 - cache: poetry - - name: Install dependencies - run: poetry install - - name: Build packages - run: poetry build - - name: Configure Poetry - run: | - poetry config repositories.testpypi https://test.pypi.org/legacy/ - poetry config pypi-token.testpypi ${{ secrets.TEST_PYPI_API_TOKEN }} - - name: Publish to test PyPI - if: ${{ github.event_name == 'push' }} - run: poetry publish -r testpypi \ No newline at end of file diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..622df93 --- /dev/null +++ b/Makefile @@ -0,0 +1,21 @@ +SHELL := /bin/bash + +.DEFAULT_GOAL := help + +SRC_DIRS := sigmaiq/llm examples +PYTHON_FILES := $(shell find $(SRC_DIRS) -type f -name "*.py") + +.PHONY: help +help: + @echo "Usage:" + @awk 'BEGIN {FS = ":.*?## "} /^[a-zA-Z0-9_-]+:.*?## / {printf " %-20s%s\n", $$1, $$2}' $(MAKEFILE_LIST) + +.PHONY: format +format: ## Format Python files + @echo "Formatting Python files..." + black $(PYTHON_FILES) + +.PHONY: ruff +ruff: ## Run Ruff linter + @echo "Running Ruff linter..." + ruff check $(PYTHON_FILES) \ No newline at end of file diff --git a/examples/custom_field_mappings.py b/examples/custom_field_mappings.py index 993028a..e296418 100644 --- a/examples/custom_field_mappings.py +++ b/examples/custom_field_mappings.py @@ -1,46 +1,47 @@ -# %% This example shows how to use the SigmAIQ pySigma wrapper to provide custom field mappings for a backend -# %% This will allow you to translate specific field names to custom field names during rule translation - -# %% Import SigmAIQ -from sigmaiq import SigmAIQBackend, SigmAIQPipeline - -# %% Import pprint for pretty printing, and copy for copying rules -from pprint import pprint -from copy import copy - -# %% A basic Sigma Rule in YAML str to convert to a query. -# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects - -sigma_rule = """ -title: whoami Command -description: Detects a basic whoami commandline execution -logsource: - product: windows - category: process_creation -detection: - selection1: - - CommandLine|contains: 'whoami.exe' - condition: selection1 -""" - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query -sigmaiq_backend = SigmAIQBackend(backend="splunk").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% Create custom field mappings -# %% This will map the CommandLine field to a custom field named "CustomCommandLine" -custom_field_mappings = {"CommandLine": "CustomCommandLine"} -my_custom_pipeline = SigmAIQPipeline.from_fieldmap(custom_field_mappings, priority=0).create_pipeline() - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query with our custom field mappings -sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline=my_custom_pipeline).create_backend() - -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query with Custom Fieldmappings: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") +# %% This example shows how to use the SigmAIQ pySigma wrapper to provide custom field mappings for a backend +# %% This will allow you to translate specific field names to custom field names during rule translation + +from copy import copy + +# %% Import pprint for pretty printing, and copy for copying rules +from pprint import pprint + +# %% Import SigmAIQ +from sigmaiq import SigmAIQBackend, SigmAIQPipeline + +# %% A basic Sigma Rule in YAML str to convert to a query. +# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects + +sigma_rule = """ +title: whoami Command +description: Detects a basic whoami commandline execution +logsource: + product: windows + category: process_creation +detection: + selection1: + - CommandLine|contains: 'whoami.exe' + condition: selection1 +""" + +# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query +sigmaiq_backend = SigmAIQBackend(backend="splunk").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nM365Defender Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% Create custom field mappings +# %% This will map the CommandLine field to a custom field named "CustomCommandLine" +custom_field_mappings = {"CommandLine": "CustomCommandLine"} +my_custom_pipeline = SigmAIQPipeline.from_fieldmap(custom_field_mappings, priority=0).create_pipeline() + +# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query with our custom field mappings +sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline=my_custom_pipeline).create_backend() + +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nM365Defender Query with Custom Fieldmappings: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") diff --git a/examples/llm_basic_usage.py b/examples/llm_basic_usage.py index 23202dd..db80714 100644 --- a/examples/llm_basic_usage.py +++ b/examples/llm_basic_usage.py @@ -1,36 +1,37 @@ -# %% This example will demonstrate how to use SigmAIQ to perform the following: -# %% 1. Download the latest Sigma Rule package release -# %% 2. Create embeddings of the Sigma Rules in the package -# %% 3. Create and save a VectorDB of the Sigma Rule embeddings -# %% 4. Use a similarity search on the VectorDB to find Sigma Rules similar to a provided query -from pprint import pprint - -# %% NOTE, this example uses OpenAI for embeddings. Ensure you have an OpenAI API key set in your environment variable -# %% OPENAI_API_KEY - -# %% Also ensure you have installed the correct requirements with: -# `pip install -r requirements/common.txt -r requirements/llm.txt` - - -# %% Import SigmAIQ LLM and OpenAIEmbeddings -from sigmaiq.llm.base import SigmaLLM - -# %% Create a SigmaLLM object with default settings. See the class docstring for more information -sigma_llm = SigmaLLM() - -# %% The `create_sigma_vectordb()` method will automatically do all the work for you :) (only run this once) -sigma_llm.create_sigma_vectordb(save=True) # Save locally to disk - -# %% Run a similarity search on the vectordb for encoded powershell commands and print top 3 results -query = "Encoded powershell commands" -matching_rules = sigma_llm.simple_search(query, k=3) -for matching_rule in matching_rules: - print(matching_rule.page_content, end="\n\n-------------------\n\n") - -# %% You can also load an existing vector store from disk (recommended) -sigma_llm.load_sigma_vectordb() - -query = "certutil" -matching_rules = sigma_llm.simple_search(query, k=3) -for matching_rule in matching_rules: - print(matching_rule.page_content, end="\n\n-------------------\n\n") +# %% This example will demonstrate how to use SigmAIQ to perform the following: +# %% 1. Download the latest Sigma Rule package release +# %% 2. Create embeddings of the Sigma Rules in the package +# %% 3. Create and save a VectorDB of the Sigma Rule embeddings +# %% 4. Use a similarity search on the VectorDB to find Sigma Rules similar to a provided query + +# %% NOTE, this example uses OpenAI for embeddings. Ensure you have an OpenAI API key set in your environment variable +# %% OPENAI_API_KEY + +# %% Also ensure you have installed the correct requirements with: +# `pip install -r requirements/common.txt -r requirements/llm.txt` + + +# %% Create a SigmaLLM object with default settings. See the class docstring for more information +from langchain_openai import OpenAIEmbeddings + +# %% Import SigmAIQ LLM and OpenAIEmbeddings +from sigmaiq.llm.base import SigmaLLM + +sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings(model="text-embedding-3-large")) + +# %% The `create_sigma_vectordb()` method will automatically do all the work for you :) (only run this once) +sigma_llm.create_sigma_vectordb(save=True) # Save locally to disk + +# %% Run a similarity search on the vectordb for encoded powershell commands and print top 3 results +query = "Encoded powershell commands" +matching_rules = sigma_llm.simple_search(query, k=3) +for matching_rule in matching_rules: + print(matching_rule.page_content, end="\n\n-------------------\n\n") + +# %% You can also load an existing vector store from disk (recommended) +sigma_llm.load_sigma_vectordb() + +query = "certutil" +matching_rules = sigma_llm.simple_search(query, k=3) +for matching_rule in matching_rules: + print(matching_rule.page_content, end="\n\n-------------------\n\n") diff --git a/examples/llm_rule_translation_and_creation.py b/examples/llm_rule_translation_and_creation.py index ce5410a..908dfcd 100644 --- a/examples/llm_rule_translation_and_creation.py +++ b/examples/llm_rule_translation_and_creation.py @@ -1,59 +1,110 @@ -# %% This example will demonstrate how to create a Sigma langchain agent chatbot, which can perform various tasks like -# %% automatically translate a rule for you, and create new rules from a users input. - -# %% Import required SigmAIQ classes and functions -from sigmaiq.llm.toolkits.base import create_sigma_agent -from sigmaiq.llm.base import SigmaLLM - -# %% Ensure we have our Sigma vector store setup with our base LLM class -sigma_llm = SigmaLLM() - -try: - sigma_llm.load_sigma_vectordb() -except Exception as e: - print(e) - print("Creating new Sigma VectorDB") - sigma_llm.create_sigma_vectordb(save=True) - -# %% Create a Sigma Agent Executor, and pass it our Sigma VectorDB -sigma_agent_executor = create_sigma_agent(sigma_vectorstore=sigma_llm.sigmadb) - -# %% RULE TRANSLATION -# %% Have the agent automatically translate a Sigma rule to a Splunk query with the splunk_cim_dm pipeline - -sigma_rule = r""" -title: whoami Command -description: Detects a basic whoami commandline execution -logsource: - product: windows - category: process_creation -detection: - selection1: - - CommandLine|contains: 'whoami.exe' - condition: selection1 -""" - -user_input = ( - "Translate the following Sigma rule to a Splunk query using the 'splunk_cim_dm' pipeline: \n\n" + sigma_rule -) - -answer = sigma_agent_executor.invoke({"input": user_input}) -print("\nRULE TRANSLATION:", end="\n\n") -print(f"Question:\n {user_input}", end="\n\n") -print(f"Answer: \n") -print(answer.get("output"), end="\n\n") - -# %% RULE CREATION -# %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand -# %% new rule based on the context of the users input and the similar Sigma Rules. - -user_input = ( - "Create a Windows process creation Sigma Rule for certutil downloading a file " - "from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query." -) - -answer = sigma_agent_executor.invoke({"input": user_input}) -print("\nRULE CREATION:", end="\n\n") -print(f"Question:\n {user_input}", end="\n\n") -print(f"Answer: \n") -print(answer.get("output"), end="\n\n") +# %% This example will demonstrate how to create a Sigma langchain agent chatbot, which can perform various tasks like +# %% automatically translate a rule for you, and create new rules from a users input. +import asyncio + +from langchain_openai import OpenAIEmbeddings + +from sigmaiq.llm.base import SigmaLLM + +# %% Import required SigmAIQ classes and functions +from sigmaiq.llm.toolkits.base import create_sigma_agent + +# %% Ensure we have our Sigma vector store setup with our base LLM class +sigma_llm = SigmaLLM(embedding_model=OpenAIEmbeddings(model="text-embedding-3-large")) + +try: + sigma_llm.load_sigma_vectordb() +except Exception as e: + print(e) + print("Creating new Sigma VectorDB") + sigma_llm.create_sigma_vectordb(save=True) + +# %% Create a Sigma Agent Executor, and pass it our Sigma VectorDB +sigma_agent_executor = create_sigma_agent(sigma_vectorstore=sigma_llm.sigmadb) + + +async def main(): + print("\n--------\nRULE TRANSLATION\n--------\n") + # %% RULE TRANSLATION + # %% Have the agent automatically translate a Sigma rule to a Splunk query with the splunk_cim_dm pipeline + user_input = ( + "Convert this Sigma rule to a Splunk query using the 'splunk_cim_dm' pipeline: \n\n" + + "title: whoami Command\n" + + "description: Detects a basic whoami commandline execution\n" + + "logsource:\n" + + " product: windows\n" + + " category: process_creation\n" + + "detection:\n" + + " selection1:\n" + + " - CommandLine|contains: 'whoami.exe'\n" + + " condition: selection1" + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"\n\nQUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE SEARCHING + # %% The agent will find official Sigma rules in the Sigma vector store based on the context of the users input. + print("\n--------\nRULE SEARCHING\n--------\n") + + user_input = ( + "What Sigma Rule can detect a process creation event where the parent process is word.exe and the child " + "process is cmd.exe?" + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% QUERY TO SIGMA RULE CONVERSION + # %% If you already have a query and want to convert it back to a Sigma Rule + print("\n--------\nQUERY TO SIGMA RULE \n--------\n") + + user_input = """ +Convert this Microsoft365Defender query into a Sigma Rule: +DeviceProcessEvents +| where ((ProcessCommandLine contains "powershell.exe -enc" or ProcessCommandLine contains "cmd.exe /c" or ProcessCommandLine contains "rundll32.exe") or (InitiatingProcessFolderPath endswith "\\explorer.exe" or InitiatingProcessFolderPath endswith "\\svchost.exe")) and (not((ProcessCommandLine contains "schtasks" or ProcessCommandLine contains "tasklist"))) + +""" + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"\n\nQUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE CREATION + # %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand + # %% new rule based on the context of the users input and the similar Sigma Rules. + print("\n--------\nRULE CREATION\n--------\n") + + user_input = ( + "Create a Windows process creation Sigma Rule for certutil downloading a file " + "from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query." + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + # %% RULE CREATION + TRANSLATION + # %% The agent will take the user input, look up similar Sigma Rules in the Sigma vector store, then create a brand + # %% new rule based on the context of the users input and the similar Sigma Rules. Then convert it to a M365 query + print("\n--------\nRULE CREATION + TRANSLATION\n--------\n") + + user_input = ( + "Create a Windows process creation Sigma Rule for certutil downloading a file " + "from definitely-not-malware.com, then translate it to a Microsoft 365 Defender query." + ) + + answer = await sigma_agent_executor.ainvoke({"input": user_input}) + print(f"QUESTION:\n {user_input}", end="\n\n") + print("ANSWER: \n") + print(answer.get("output"), end="\n\n") + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/examples/translate_sigma_rules.py b/examples/translate_sigma_rules.py index 21402e5..ec88101 100644 --- a/examples/translate_sigma_rules.py +++ b/examples/translate_sigma_rules.py @@ -1,77 +1,77 @@ -# %% This example shows how to use the SigmAIQ pySigma wrapper to easily translate Sigma rules to queries -# %% easily, without having to worry about installing and configuring the correct backends, pipelines and other details. - - -# %% Import SigmAIQ -from sigmaiq import SigmAIQBackend - -# %% Import pprint for pretty printing, and copy for copying rules -from pprint import pprint -from copy import copy - -# %% A basic Sigma Rule in YAML str to convert to a query. -# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects - -sigma_rule = """ -title: whoami Command -description: Detects a basic whoami commandline execution -logsource: - product: windows - category: process_creation -detection: - selection1: - - CommandLine|contains: 'whoami.exe' - condition: selection1 -""" - -# %% BACKENDS -# %% Show the available supported backends -print("Supported Backends:", end="\n\n") -pprint(SigmAIQBackend.display_available_backends()) -print("\n-------------------") - -# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query -# %% SigmAIQ will automatically select the best pipeline for the backend -sigmaiq_backend = SigmAIQBackend(backend="microsoft365defender").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries - -print("\nM365Defender Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% PIPELINES -# %% Show the available pipelines with each backend -print("Available Pipelines:", end="\n\n") -pprint(SigmAIQBackend.display_all_associated_pipelines()) -print("\n-------------------") - -# %% Create SigmAIQ backend translate the rule to a Splunk search with the CIM pipeline -sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline="splunk_cim_dm").create_backend() -query = sigmaiq_backend.translate(copy(sigma_rule)) - -print("\nSplunk CIM Query: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - -# %% OUTPUT FORMATS -# %% Show the available output formats with each backend -print("\nAvailable Output Formats:", end="\n\n") -pprint(SigmAIQBackend.display_backends_and_outputs()) -print("\n-------------------") - -# %% Change the output_format to an Enterprise Security Correlation Search stanza -sigmaiq_backend.set_output_format("stanza") -query = sigmaiq_backend.translate(copy(sigma_rule)) - -print("\nSplunk CIM Query, Stanza Output: ", end="\n\n") -pprint(query[0]) -print("\n-------------------") - - -# %% You can also translate a Sigma rule to all supported backend, pipeline, and output format combinations at once. -# %% Any combination that is not supported will not be included in the results -# %% This is useful for testing and comparing the output of different backends and pipelines -queries = SigmAIQBackend.create_all_and_translate(copy(sigma_rule)) - -print("\n All Translations: ", end="\n\n") -pprint(queries) +# %% This example shows how to use the SigmAIQ pySigma wrapper to easily translate Sigma rules to queries +# %% easily, without having to worry about installing and configuring the correct backends, pipelines and other details. + + +# %% Import SigmAIQ +from sigmaiq import SigmAIQBackend + +# %% Import pprint for pretty printing, and copy for copying rules +from pprint import pprint +from copy import copy + +# %% A basic Sigma Rule in YAML str to convert to a query. +# %% SigmAIQ also accepts a rule in JSON/Dict format, SigmaRule objects, and SigmaCollection objects + +sigma_rule = """ +title: whoami Command +description: Detects a basic whoami commandline execution +logsource: + product: windows + category: process_creation +detection: + selection1: + - CommandLine|contains: 'whoami.exe' + condition: selection1 +""" + +# %% BACKENDS +# %% Show the available supported backends +print("Supported Backends:", end="\n\n") +pprint(SigmAIQBackend.display_available_backends()) +print("\n-------------------") + +# %% Create SigmAIQ backend translate the rule to a Microsoft 365 Defender query +# %% SigmAIQ will automatically select the best pipeline for the backend +sigmaiq_backend = SigmAIQBackend(backend="microsoft_xdr").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) # Returns List of queries + +print("\nM365Defender Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% PIPELINES +# %% Show the available pipelines with each backend +print("Available Pipelines:", end="\n\n") +pprint(SigmAIQBackend.display_all_associated_pipelines()) +print("\n-------------------") + +# %% Create SigmAIQ backend translate the rule to a Splunk search with the CIM pipeline +sigmaiq_backend = SigmAIQBackend(backend="splunk", processing_pipeline="splunk_cim_dm").create_backend() +query = sigmaiq_backend.translate(copy(sigma_rule)) + +print("\nSplunk CIM Query: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + +# %% OUTPUT FORMATS +# %% Show the available output formats with each backend +print("\nAvailable Output Formats:", end="\n\n") +pprint(SigmAIQBackend.display_backends_and_outputs()) +print("\n-------------------") + +# %% Change the output_format to an Enterprise Security Correlation Search stanza +sigmaiq_backend.set_output_format("stanza") +query = sigmaiq_backend.translate(copy(sigma_rule)) + +print("\nSplunk CIM Query, Stanza Output: ", end="\n\n") +pprint(query[0]) +print("\n-------------------") + + +# %% You can also translate a Sigma rule to all supported backend, pipeline, and output format combinations at once. +# %% Any combination that is not supported will not be included in the results +# %% This is useful for testing and comparing the output of different backends and pipelines +queries = SigmAIQBackend.create_all_and_translate(copy(sigma_rule)) + +print("\n All Translations: ", end="\n\n") +pprint(queries) diff --git a/poetry.lock b/poetry.lock index 1e6e2ea..7051368 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,136 +2,122 @@ [[package]] name = "aiohappyeyeballs" -version = "2.4.3" +version = "2.4.4" description = "Happy Eyeballs for asyncio" optional = false python-versions = ">=3.8" files = [ - {file = "aiohappyeyeballs-2.4.3-py3-none-any.whl", hash = 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-pysigma-backend-elasticsearch = "1.1.2" -pysigma-backend-insightidr = "0.2.4" -pysigma-backend-kusto = "0.4.0" -pysigma-backend-loki = "0.12.2" -pysigma-backend-netwitness = "0.1.3" -pysigma-backend-opensearch = "1.0.2" -pysigma-backend-qradar-aql = "0.3.2" -pysigma-backend-sentinelone = "0.1.3" -pysigma-backend-splunk = "1.1.0" -pysigma-backend-stix2 = "0.2.0" -pysigma-pipeline-sysmon = "1.0.4" -pysigma-pipeline-windows = "1.2.0" -importlib-resources = "^5.13.0" +six = "*" +pysigma = "^0.11.14" +certifi = "*" +pysigma-backend-carbonblack = "^0.1.8" +pysigma-backend-cortexxdr = "^0.1.4" +pysigma-backend-crowdstrike = "^2.0.0" +pysigma-backend-elasticsearch = "^1.1.2" +pysigma-backend-insightidr = "^0.2.4" +pysigma-backend-kusto = "^0.4.0" +pysigma-backend-loki = "^0.12.0" +pysigma-backend-netwitness = "^0.1.3" +pysigma-backend-opensearch = "^1.0.2" +pysigma-backend-qradar-aql = "^0.3.2" +pysigma-backend-sentinelone = "^0.1.3" +pysigma-backend-splunk = "^1.1.0" +pysigma-backend-stix2 = "^0.2.0" +pysigma-pipeline-sysmon = "^1.0.4" +pysigma-pipeline-windows = "^1.2.0" +importlib-resources = "*" +openai = "*" +tiktoken = "*" +faiss-cpu = "*" +langchain = "*" +langchain-community = "*" +langchain-openai = "*" +langchain-core = "*" -[tool.poetry.group.llm] -optional = true -[tool.poetry.group.llm.dependencies] -langchain = "^0.0.335" -openai = "^1.2.4" -tiktoken = "^0.5.1" -faiss-cpu = "^1.7.4" - -[tool.poetry.group.dev] -optional = true - -[tool.poetry.group.dev.dependencies] -pytest = "^8.3.3" -pytest-cov = "^5.0.0" -black = "^24.8.0" -ruff = "^0.6.8" +[tool.poetry.dev-dependencies] +pytest = "*" +pytest-cov = "*" +black = "*" +ruff = "*" [build-system] requires = ["poetry-core"] @@ -64,45 +61,6 @@ build-backend = "poetry.core.masonry.api" [tool.black] line-length = 120 -target-version = ['py311'] - -[tool.ruff] -# Exclude a variety of commonly ignored directories. -exclude = [ - ".bzr", - ".direnv", - ".eggs", - ".git", - ".git-rewrite", - ".hg", - ".ipynb_checkpoints", - ".mypy_cache", - ".nox", - ".pants.d", - ".pyenv", - ".pytest_cache", - ".pytype", - ".ruff_cache", - ".svn", - ".tox", - ".venv", - ".vscode", - "__pypackages__", - "_build", - "buck-out", - "build", - "dist", - "node_modules", - "site-packages", - "venv", - ".venv" -] - -# Same as Black. -line-length = 120 - -# Assume Python 3.11 -target-version = "py311" [tool.coverage.run] omit = [ diff --git a/requirements/common.txt b/requirements/common.txt index a84902c..60512e3 100644 --- a/requirements/common.txt +++ b/requirements/common.txt @@ -1,25 +1,28 @@ -certifi==2023.7.22 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -idna==3.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -importlib-resources==5.13.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pyparsing==3.1.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-carbonblack==0.1.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-cortexxdr==0.1.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-elasticsearch==1.0.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-insightidr==0.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-loki==0.9.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-microsoft365defender==0.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-opensearch==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-qradar-aql==0.2.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-sentinelone==0.1.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-splunk==1.0.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-backend-stix==0.1.8 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-pipeline-crowdstrike==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-pipeline-sysmon==1.0.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma-pipeline-windows==1.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pysigma==0.9.11 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -urllib3==2.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -zipp==3.17.0 ; python_full_version >= "3.8.1" and python_version < "3.10" +certifi==2024.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +idna==3.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +importlib-resources==5.13.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +jinja2==3.1.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +markupsafe==2.1.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pyparsing==3.1.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-carbonblack==0.1.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-cortexxdr==0.1.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-elasticsearch==1.0.10 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-insightidr==0.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-loki==0.10.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-microsoft365defender==0.2.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-opensearch==1.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-qradar-aql==0.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-sentinelone==0.1.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-splunk==1.0.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-backend-stix2==0.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-pipeline-crowdstrike==1.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-pipeline-sysmon==1.0.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma-pipeline-windows==1.1.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pysigma==0.10.10 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +regex==2023.12.25 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +urllib3==2.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +zipp==3.18.2 ; python_full_version >= "3.8.1" and python_version < "3.10" diff --git a/requirements/dev.txt b/requirements/dev.txt new file mode 100644 index 0000000..2ef8d66 --- /dev/null +++ b/requirements/dev.txt @@ -0,0 +1,16 @@ +black==24.4.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +click==8.1.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +colorama==0.4.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and sys_platform == "win32" or python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and platform_system == "Windows" +coverage[toml]==7.5.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +exceptiongroup==1.2.1 ; python_full_version >= "3.8.1" and python_version < "3.11" +iniconfig==2.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +mypy-extensions==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pathspec==0.12.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +platformdirs==4.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pluggy==1.5.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pytest-cov==4.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pytest==7.4.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +ruff==0.0.286 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +tomli==2.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.0a6" +typing-extensions==4.11.0 ; python_full_version >= "3.8.1" and python_version < "3.11" diff --git a/requirements/llm.txt b/requirements/llm.txt index 565b617..33a6b33 100644 --- a/requirements/llm.txt +++ b/requirements/llm.txt @@ -1,60 +1,43 @@ -aiohttp==3.8.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -aiosignal==1.3.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -annotated-types==0.6.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -anyio==3.7.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -async-timeout==4.0.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -attrs==23.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -backoff==2.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -beautifulsoup4==4.12.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -certifi==2023.7.22 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -chardet==5.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -click==8.1.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -colorama==0.4.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" and platform_system == "Windows" -dataclasses-json==0.6.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -distro==1.8.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -emoji==2.8.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -exceptiongroup==1.1.3 ; python_full_version >= "3.8.1" and python_version < "3.11" -faiss-cpu==1.7.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -filetype==1.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -frozenlist==1.4.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -greenlet==3.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" and (platform_machine == "win32" or platform_machine == "WIN32" or platform_machine == "AMD64" or platform_machine == "amd64" or platform_machine == "x86_64" or platform_machine == "ppc64le" or platform_machine == "aarch64") -h11==0.14.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -httpcore==1.0.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -httpx==0.25.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -idna==3.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -joblib==1.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -jsonpatch==1.33 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -jsonpointer==2.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -langchain==0.0.335 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -langdetect==1.0.9 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -langsmith==0.0.64 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -lxml==4.9.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -marshmallow==3.20.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -multidict==6.0.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -mypy-extensions==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -nltk==3.8.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -numpy==1.24.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -openai==1.2.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pydantic-core==2.14.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pydantic==2.5.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -python-iso639==2023.6.15 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -python-magic==0.4.27 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -rapidfuzz==3.5.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -regex==2023.10.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -six==1.16.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -sniffio==1.3.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -soupsieve==2.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -sqlalchemy==2.0.23 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -tabulate==0.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -tenacity==8.2.3 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -tiktoken==0.5.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -tqdm==4.66.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -typing-extensions==4.8.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -typing-inspect==0.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -unstructured==0.10.30 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -urllib3==2.1.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" -yarl==1.9.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.6" +aiohttp==3.9.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +aiosignal==1.3.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +annotated-types==0.6.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +anyio==3.7.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +async-timeout==4.0.3 ; python_full_version >= "3.8.1" and python_version < "3.11" +attrs==23.2.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +certifi==2024.2.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +charset-normalizer==3.3.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +colorama==0.4.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" and platform_system == "Windows" +dataclasses-json==0.6.6 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +distro==1.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +exceptiongroup==1.2.1 ; python_full_version >= "3.8.1" and python_version < "3.11" +faiss-cpu==1.8.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +frozenlist==1.4.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +greenlet==3.0.3 ; platform_machine == "aarch64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "ppc64le" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "x86_64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "amd64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "AMD64" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "win32" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" or platform_machine == "WIN32" and python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +h11==0.14.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +httpcore==1.0.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +httpx==0.27.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +idna==3.7 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +jsonpatch==1.33 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +jsonpointer==2.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +langchain==0.0.335 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +langsmith==0.0.92 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +marshmallow==3.21.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +multidict==6.0.5 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +mypy-extensions==1.0.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +numpy==1.24.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +openai==1.30.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +packaging==22.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pydantic-core==2.18.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pydantic==2.7.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +pyyaml==6.0.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +regex==2023.12.25 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +requests==2.31.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +sniffio==1.3.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +sqlalchemy==2.0.30 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +tenacity==8.3.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +tiktoken==0.5.2 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +tqdm==4.66.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +typing-extensions==4.11.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +typing-inspect==0.9.0 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +urllib3==2.2.1 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" +yarl==1.9.4 ; python_full_version >= "3.8.1" and python_full_version <= "3.11.9" diff --git a/sigmaiq/llm/README.md b/sigmaiq/llm/README.md index a4fbe65..71885ea 100644 --- a/sigmaiq/llm/README.md +++ b/sigmaiq/llm/README.md @@ -18,6 +18,7 @@ Currently, the use cases of this feature include: - Embedding creation and storage of Sigma Rules - Sigma Rule similarity searching - Agent/Bot for Sigma Rule translation and creation +- Converting backend queries to Sigma Rules Please see the `examples` folder for use case examples. diff --git a/sigmaiq/llm/base.py b/sigmaiq/llm/base.py index 2e083f7..aa87604 100644 --- a/sigmaiq/llm/base.py +++ b/sigmaiq/llm/base.py @@ -2,21 +2,24 @@ import os from typing import Type, List -# sigmaiq -from sigmaiq.utils.sigma.rule_updater import SigmaRuleUpdater -from sigmaiq.globals import DEFAULT_DIRS - -# langchain -from langchain.embeddings import OpenAIEmbeddings -from langchain.vectorstores import FAISS -from langchain.document_loaders import DirectoryLoader, TextLoader -from langchain.text_splitter import CharacterTextSplitter +from langchain.docstore.document import Document +from langchain.document_loaders.base import BaseLoader +from langchain.schema.document import BaseDocumentTransformer # langchain typing from langchain.schema.embeddings import Embeddings from langchain.schema.vectorstore import VectorStore -from langchain.document_loaders.base import BaseLoader, Document -from langchain.schema.document import BaseDocumentTransformer +from langchain.text_splitter import CharacterTextSplitter +from langchain_community.document_loaders import DirectoryLoader, TextLoader +from langchain_community.vectorstores import FAISS + +# langchain +from langchain_openai import OpenAIEmbeddings + +from sigmaiq.globals import DEFAULT_DIRS + +# sigmaiq +from sigmaiq.utils.sigma.rule_updater import SigmaRuleUpdater class SigmaLLM(SigmaRuleUpdater): @@ -34,7 +37,8 @@ def __init__( self, rule_dir: str = None, vector_store_dir: str = None, - embedding_function: Type[Embeddings] = OpenAIEmbeddings, + embedding_model: OpenAIEmbeddings = None, + embedding_function: Type[Embeddings] = OpenAIEmbeddings, # TODO RS : Consolidate this with embedding_model vector_store: Type[VectorStore] = FAISS, rule_loader: Type[BaseLoader] = DirectoryLoader, rule_splitter: Type[BaseDocumentTransformer] = CharacterTextSplitter, @@ -60,7 +64,10 @@ def __init__( # Setup rest of class self.vector_store_dir = self._setup_vector_store_dir(vector_store_dir) - self.embedding_function = embedding_function() + if embedding_model: + self.embedding_function = embedding_model + else: + self.embedding_function = embedding_function() self.vector_store = vector_store self.sigmadb = None self.rule_loader = rule_loader @@ -75,6 +82,7 @@ def load_sigma_vectordb(self): self.sigmadb = self.vector_store.load_local( # CHANGE ME IF NEEDED folder_path=self.vector_store_dir, embeddings=self.embedding_function, + allow_dangerous_deserialization=True, ) except Exception as e: raise e diff --git a/sigmaiq/llm/toolkits/base.py b/sigmaiq/llm/toolkits/base.py index c6d02f6..a4b3ee6 100644 --- a/sigmaiq/llm/toolkits/base.py +++ b/sigmaiq/llm/toolkits/base.py @@ -1,132 +1,139 @@ -# stdlib -from typing import Optional, Dict, Any, Type, Union -import json -from json import JSONDecodeError - -# langchain -from langchain.agents.agent import AgentExecutor -from langchain.agents.format_scratchpad import format_to_openai_function_messages -from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser -from langchain.chat_models import ChatOpenAI -from langchain.prompts import ChatPromptTemplate -from langchain.tools.render import format_tool_to_openai_function - -# langchain typing -from langchain.schema import ( - AgentAction, - AgentFinish, - OutputParserException, -) -from langchain.schema.vectorstore import VectorStore -from langchain.schema.language_model import BaseLanguageModel -from langchain.schema.agent import AgentActionMessageLog -from langchain.schema.messages import ( - AIMessage, - BaseMessage, -) - -# sigmaiq -from sigmaiq.llm.toolkits.sigma_toolkit import SigmaToolkit -from sigmaiq.llm.toolkits.prompts import SIGMA_AGENT_PROMPT - - -def create_sigma_agent( - agent_llm: BaseLanguageModel = ChatOpenAI(model="gpt-3.5-turbo"), - rule_creation_llm: BaseLanguageModel = ChatOpenAI(model="gpt-3.5-turbo"), - sigma_vectorstore: VectorStore = None, - toolkit: Type[SigmaToolkit] = SigmaToolkit, - prompt: Optional[ChatPromptTemplate] = SIGMA_AGENT_PROMPT, - verbose: bool = False, - return_intermediate_steps: bool = False, - agent_executor_kwargs: Optional[Dict[str, Any]] = None, -) -> AgentExecutor: - """Construct a Sigma agent from an LLM and tools. - - Args: - agent_llm (BaseLanguageModel, optional): The LLM to use for the agent. Defaults to ChatOpenAI(model="gpt-3.5-turbo"). - rule_creation_llm (BaseLanguageModel, optional): The LLM to use for the rule creation tool. Defaults to ChatOpenAI(model="gpt-3.5-turbo"). - sigma_vectorstore (VectorStore, optional): The vectorstore containing Sigma rules to use for the agent. Defaults to None. - toolkit (Type[SigmaToolkit], optional): The toolkit to use for the agent. Defaults to SigmaToolkit. - prompt (Optional[ChatPromptTemplate], optional): The prompt to use for the agent. Defaults to SIGMA_AGENT_PROMPT. - verbose (bool, optional): Whether to print verbose output. Defaults to False. - return_intermediate_steps (bool, optional): Whether to return intermediate steps. Defaults to False. - agent_executor_kwargs (Optional[Dict[str, Any]], optional): Additional kwargs to pass to the AgentExecutor. Defaults to None. - - Returns: - AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response - """ # noqa: E501 - - # Get Sigma Tools from the SigmaToolkit. Init with sigma vectorstore and rule creation llm - tools = toolkit(sigmadb=sigma_vectorstore, rule_creation_llm=rule_creation_llm).get_tools() - - # Create OpenAI Function for each tool for the agent LLM, so we can create an OpenAI Function AgentExecutor - llm_with_tools = agent_llm.bind(functions=[format_tool_to_openai_function(t) for t in tools]) - - # Create the agent - agent = ( - { - "input": lambda x: x["input"], - "agent_scratchpad": lambda x: format_to_openai_function_messages(x["intermediate_steps"]), - } - | prompt - | llm_with_tools - | CustomOpenAIFunctionsAgentOutputParser() - ) - - # Create and return the AgentExecutor - agent_executor = AgentExecutor( - agent=agent, - tools=tools, - verbose=verbose, - return_intermediate_steps=return_intermediate_steps, - handle_parsing_errors=True, - **(agent_executor_kwargs or {}), - ) - - return agent_executor - - -class CustomOpenAIFunctionsAgentOutputParser(OpenAIFunctionsAgentOutputParser): - """Custom OpenAIFunctionsAgentOutputParser to overcome the JSON parsing error on some agent - intermediate step inputs. This occurs because the `json.load()` method needs the arg `strict=False` to - parse the JSON. This is a hacky way to do this, but it works for now. - """ - # Override - @staticmethod - def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: - """Parse an AI message.""" - if not isinstance(message, AIMessage): - raise TypeError(f"Expected an AI message got {type(message)}") - - function_call = message.additional_kwargs.get("function_call", {}) - - if function_call: - function_name = function_call["name"] - try: - _tool_input = json.loads(function_call["arguments"].strip(), strict=False) # HACK - except JSONDecodeError: - raise OutputParserException( - f"Could not parse tool input: {function_call} because " f"the `arguments` is not valid JSON." - ) - - # HACK HACK HACK: - # The code that encodes tool input into Open AI uses a special variable - # name called `__arg1` to handle old style tools that do not expose a - # schema and expect a single string argument as an input. - # We unpack the argument here if it exists. - # Open AI does not support passing in a JSON array as an argument. - if "__arg1" in _tool_input: - tool_input = _tool_input["__arg1"] - else: - tool_input = _tool_input - - content_msg = f"responded: {message.content}\n" if message.content else "\n" - log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n" - return AgentActionMessageLog( - tool=function_name, - tool_input=tool_input, - log=log, - message_log=[message], - ) - - return AgentFinish(return_values={"output": message.content}, log=str(message.content)) +# stdlib +import json +from json import JSONDecodeError +from typing import Optional, Dict, Any, Type, Union + +# langchain +from langchain.agents.agent import AgentExecutor +from langchain.agents.format_scratchpad import format_to_openai_function_messages +from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser +from langchain.prompts import ChatPromptTemplate + +# langchain typing +from langchain.schema import ( + AgentAction, + AgentFinish, + OutputParserException, +) +from langchain.schema.agent import AgentActionMessageLog +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.messages import ( + AIMessage, + BaseMessage, +) +from langchain.schema.vectorstore import VectorStore +from langchain_core.utils.function_calling import convert_to_openai_function +from langchain_openai import ChatOpenAI + +from sigmaiq.llm.toolkits.prompts import SIGMA_AGENT_PROMPT + +# sigmaiq +from sigmaiq.llm.toolkits.sigma_toolkit import SigmaToolkit + + +def create_sigma_agent( + agent_llm: BaseLanguageModel = ChatOpenAI(model="gpt-4o"), + rule_creation_llm: BaseLanguageModel = ChatOpenAI(model="gpt-4o"), + sigma_vectorstore: VectorStore = None, + toolkit: Type[SigmaToolkit] = SigmaToolkit, + prompt: Optional[ChatPromptTemplate] = SIGMA_AGENT_PROMPT, + verbose: bool = False, + return_intermediate_steps: bool = False, + agent_executor_kwargs: Optional[Dict[str, Any]] = None, +) -> AgentExecutor: + """Construct a Sigma agent from an LLM and tools. + + Args: + agent_llm (BaseLanguageModel, optional): The LLM to use for the agent. Defaults to ChatOpenAI(model="gpt-4o"). + rule_creation_llm (BaseLanguageModel, optional): The LLM to use for the rule creation tool. Defaults to ChatOpenAI(model="gpt-4o"). + sigma_vectorstore (VectorStore, optional): The vectorstore containing Sigma rules to use for the agent. Defaults to None. + toolkit (Type[SigmaToolkit], optional): The toolkit to use for the agent. Defaults to SigmaToolkit. + prompt (Optional[ChatPromptTemplate], optional): The prompt to use for the agent. Defaults to SIGMA_AGENT_PROMPT. + verbose (bool, optional): Whether to print verbose output. Defaults to False. + return_intermediate_steps (bool, optional): Whether to return intermediate steps. Defaults to False. + agent_executor_kwargs (Optional[Dict[str, Any]], optional): Additional kwargs to pass to the AgentExecutor. Defaults to None. + + Returns: + AgentExecutor: Returns a callable AgentExecutor object. Either you can call it or use run method with the query to get the response + """ # noqa: E501 + + # Get Sigma Tools from the SigmaToolkit. Init with sigma vectorstore and rule creation llm + tools = toolkit(sigmadb=sigma_vectorstore, rule_creation_llm=rule_creation_llm).get_tools() + + # Assert if any of the tools does not have arun + for tool in tools: + assert hasattr(tool, "arun"), f"Tool {tool.name} does not have an 'arun' method" + + # Create OpenAI Function for each tool for the agent LLM, so we can create an OpenAI Function AgentExecutor + llm_with_tools = agent_llm.bind(functions=[convert_to_openai_function(t) for t in tools]) + + # Create the agent + agent = ( + { + "input": lambda x: x["input"], + "chat_history": lambda x: x.get("chat_history", []), + "agent_scratchpad": lambda x: format_to_openai_function_messages(x["intermediate_steps"]), + } + | prompt + | llm_with_tools + | CustomOpenAIFunctionsAgentOutputParser() + ) + + # Create and return the AgentExecutor + agent_executor = AgentExecutor( + agent=agent, + tools=tools, + verbose=verbose, + return_intermediate_steps=return_intermediate_steps, + handle_parsing_errors=True, + **(agent_executor_kwargs or {}), + ) + + return agent_executor + + +class CustomOpenAIFunctionsAgentOutputParser(OpenAIFunctionsAgentOutputParser): + """Custom OpenAIFunctionsAgentOutputParser to overcome the JSON parsing error on some agent + intermediate step inputs. This occurs because the `json.load()` method needs the arg `strict=False` to + parse the JSON. This is a hacky way to do this, but it works for now. + """ + + # Override + @staticmethod + def _parse_ai_message(message: BaseMessage) -> Union[AgentAction, AgentFinish]: + """Parse an AI message.""" + if not isinstance(message, AIMessage): + raise TypeError(f"Expected an AI message got {type(message)}") + + function_call = message.additional_kwargs.get("function_call", {}) + + if function_call: + function_name = function_call["name"] + try: + _tool_input = json.loads(function_call["arguments"].strip(), strict=False) # HACK + except JSONDecodeError: + raise OutputParserException( + f"Could not parse tool input: {function_call} because " f"the `arguments` is not valid JSON." + ) + + # HACK HACK HACK: + # The code that encodes tool input into Open AI uses a special variable + # name called `__arg1` to handle old style tools that do not expose a + # schema and expect a single string argument as an input. + # We unpack the argument here if it exists. + # Open AI does not support passing in a JSON array as an argument. + if "__arg1" in _tool_input: + tool_input = _tool_input["__arg1"] + else: + tool_input = _tool_input + + content_msg = f"responded: {message.content}\n" if message.content else "\n" + log = f"\nInvoking: `{function_name}` with `{tool_input}`\n{content_msg}\n" + return AgentActionMessageLog( + tool=function_name, + tool_input=tool_input, + log=log, + message_log=[message], + ) + + return AgentFinish(return_values={"output": message.content}, log=str(message.content)) diff --git a/sigmaiq/llm/toolkits/prompts.py b/sigmaiq/llm/toolkits/prompts.py index 2beffbe..b649081 100644 --- a/sigmaiq/llm/toolkits/prompts.py +++ b/sigmaiq/llm/toolkits/prompts.py @@ -1,19 +1,35 @@ -from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder +from langchain.prompts import ( + SystemMessagePromptTemplate, + HumanMessagePromptTemplate, + ChatPromptTemplate, + MessagesPlaceholder, +) + +system_template = """ +You are a threat detection engineering assistant bot specializing in Sigma Rules. + +You have four tools at your disposal: +1. translate_sigma_rule: converts or translates a Sigma Rule into a query for a specific backend/product. +2. find_sigma_rule: Searches for a Sigma Rule in the vector database based on the users question. +3. create_sigma_rule_vectorstore: Creates new Sigma Rule from the users input, as well as rules in a sigma rule vectorstore to use as context based on the users question. If the user's question already contains a query, use 'query_to_sigma_rule' instead. +4. query_to_sigma_rule: Converts/translates a product/SIEM/backend query or search from the query language into a YAML Sigma Rule. +Do not use 'translate_sigma_rule' unless the user explicitly asks for a Sigma Rule to be converted or translated into a query for a specific backend, pipeline, and/or output format. + + +Chat History: +{chat_history} +""" +system_message_prompt = SystemMessagePromptTemplate.from_template(system_template) + +human_template = """ +Question: {input} +""" +human_message_prompt = HumanMessagePromptTemplate.from_template(human_template) -# Default prompts for Sigma agents -SIGMA_AGENT_PROMPT = prompt = ChatPromptTemplate.from_messages( +SIGMA_AGENT_PROMPT = ChatPromptTemplate.from_messages( [ - ( - "system", - "You are a threat detection engineering assistant bot specializing in Sigma rules." - "You have two tools at your disposal: translate_sigma_rule and create_sigma_rule_vectorstore." - "translate_sigma_rule will convert or translate a Sigma Rule into a query for a specific backend." - "create_sigma_rule_vectorstore will take the users input, find similar Sigma Rules from the vectorstore," - "then create a brand new Sigma Rule based on the users input and the similar Sigma Rules returned from the vectorstore" - "to use as context. The output is a Sigma Rule in YAML format. Do not use 'translate_sigma_rule' unless " - "the user explicitly asks for a Sigma Rule to be converted or translated into a query for a specific backend.", - ), - ("user", "{input}"), + system_message_prompt, + human_message_prompt, MessagesPlaceholder(variable_name="agent_scratchpad"), ] ) diff --git a/sigmaiq/llm/toolkits/sigma_toolkit.py b/sigmaiq/llm/toolkits/sigma_toolkit.py index f6935ee..6424f8d 100644 --- a/sigmaiq/llm/toolkits/sigma_toolkit.py +++ b/sigmaiq/llm/toolkits/sigma_toolkit.py @@ -12,6 +12,8 @@ # sigmaiq tools from sigmaiq.llm.tools.create_sigma_rule import CreateSigmaRuleVectorStoreTool from sigmaiq.llm.tools.translate_sigma_rule import TranslateSigmaRuleTool +from sigmaiq.llm.tools.find_sigma_rule import FindSigmaRuleTool +from sigmaiq.llm.tools.query_to_sigma_rule import QueryToSigmaRuleTool class SigmaToolkit(BaseToolkit): @@ -26,6 +28,8 @@ class Config: def get_tools(self) -> List[BaseTool]: """Get the tools in the toolkit.""" return [ + FindSigmaRuleTool(sigmadb=self.sigmadb, llm=self.rule_creation_llm), TranslateSigmaRuleTool(), CreateSigmaRuleVectorStoreTool(sigmadb=self.sigmadb, llm=self.rule_creation_llm), + QueryToSigmaRuleTool(llm=self.rule_creation_llm), ] diff --git a/sigmaiq/llm/tools/create_sigma_rule.py b/sigmaiq/llm/tools/create_sigma_rule.py index 28b5908..6fadba3 100644 --- a/sigmaiq/llm/tools/create_sigma_rule.py +++ b/sigmaiq/llm/tools/create_sigma_rule.py @@ -1,111 +1,117 @@ -# stdlib -from typing import Type, Optional - -# langchain -from langchain.callbacks.manager import CallbackManagerForToolRun -from langchain.prompts import ChatPromptTemplate -from langchain.tools import BaseTool -from langchain.pydantic_v1 import BaseModel, Field - -# langchain typing -from langchain.schema.language_model import BaseLanguageModel -from langchain.schema.runnable import RunnablePassthrough -from langchain.schema.output_parser import StrOutputParser -from langchain.schema.vectorstore import VectorStore - - -class CreateSigmaRuleInput(BaseModel): - """Input for TranslateSigmaRule tool, which uses SigmAIQ backend factory to convert a Sigma Rule into - a query for a specific backend.""" - - query: str = Field( - description="The users question, used to search through the Sigma VectorStore and create a Sigma Rule." - ) - - class Config(BaseTool.Config): - pass - - -class CreateSigmaRuleVectorStoreTool(BaseTool): - """Class for translating Sigma rules via SigmAIQ Backend Factory""" - - name: str = "create_sigma_rule_vectorstore" - args_schema: Type[BaseModel] = CreateSigmaRuleInput - description: str = """Use this tool to take the users input, find similar Sigma Rules from the vectorstore, - then create a brand new Sigma Rule based on the users input and the similar Sigma Rules returned from the vectorstore - to use as context. The output is a Sigma Rule in YAML format. - """ - sigmadb: VectorStore - llm: BaseLanguageModel - k: int = 3 - verbose = True - - def _run( - self, - query: str, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Run the tool""" - - template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rule creation. - You are assisting a user in creating a new Sigma Rule based on the users question. - The users question is first used to find similar Sigma Rules from the a vectorstore containing official - Sigma Rules. The official Sigma Rules should be used as context as needed in conjunction with the detection specified - in the users question to create a new Sigma Rule. Set the 'author' to 'SigmAIQ (AttackIQ)', - the date to today's date, and the reference to 'https://github.com/AttackIQ/SigmAIQ'. - The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field - can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, - which can be used in the condition field to select criteria and filter out criteria respectively. - - Sigma Rule Schema: - - title - id [optional] - related [optional] - - id {{rule-id}} - type {{type-identifier}} - status [optional] - description [optional] - references [optional] - author [optional] - date [optional] - modified [optional] - tags [optional] - logsource - category [optional] - product [optional] - service [optional] - definition [optional] - ... - detection - {{search-identifier}} [optional] - {{string-list}} [optional] - {{map-list}} [optional] - {{field: valu}}> [optional] - ... # Multiple search identifiers can be specified as needed and used in the condition - condition - fields [optional] - falsepositives [optional] - level [optional]: - ------- - Vectorstore Search Results: - - {context} - ------ - User's Question: - {question} - """ - - prompt = ChatPromptTemplate.from_template(template) - retriever = self.sigmadb.as_retriever(search_kwargs={"k": self.k}) - chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() - return chain.invoke(query) - - async def _arun( - self, - query: str, - k: int, - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError +# stdlib +import asyncio +from typing import Type + +# langchain +from langchain.prompts import ChatPromptTemplate + +# langchain typing +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.schema.vectorstore import VectorStore +from langchain.tools import BaseTool +from pydantic import BaseModel, Field + + +class CreateSigmaRuleInput(BaseModel): + """Input for TranslateSigmaRule tool, which uses SigmAIQ backend factory to convert a Sigma Rule into + a query for a specific backend.""" + + query: str = Field( + description="The users question, used to search through the Sigma VectorStore and create a Sigma Rule." + ) + + +class CreateSigmaRuleVectorStoreTool(BaseTool): + """Class for translating Sigma rules via SigmAIQ Backend Factory""" + + name: str = "create_sigma_rule_vectorstore" + args_schema: Type[BaseModel] = CreateSigmaRuleInput + description: str = """Use this tool to take the users input, find similar Sigma Rules from the vectorstore, +then create a brand new Sigma Rule based on the users input and the similar Sigma Rules returned from the vectorstore +to use as context. The output is a Sigma Rule in YAML format. +""" + sigmadb: VectorStore + llm: BaseLanguageModel + k: int = 3 + verbose: bool = False + + def _run(self, query: str) -> str: + """Run the tool""" + return asyncio.run(self._arun(query)) + + async def _arun(self, query: str) -> str: + """Async run the tool""" + template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rule creation. +You are assisting a user in creating a new Sigma Rule based on the users question. +The users question is first used to find similar Sigma Rules from the a vectorstore containing official +Sigma Rules. The official Sigma Rules can be used as context as needed in conjunction with the detection specified +in the users question to create a new Sigma Rule. +The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field +can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, +which can be used in the condition field to select criteria and filter out criteria respectively. +Set the 'author' to 'SigmAIQ (AttackIQ)', the date to today's date, and the reference to 'https://github.com/AttackIQ/SigmAIQ'. +If you use other rules as context and derive the created Sigma Rules from the context rules, you must +include the original authors under the 'author' field in the new rule in addition to "SigmAIQ (AttackIQ), +and add the original rule IDs under the 'related' field. The valid 'types' under 'related' are the following: + + derived: The rule was derived from the referred rule or rules, which may remain active. + obsoletes: The rule obsoletes the referred rule or rules, which aren't used anymore. + merged: The rule was merged from the referred rules. The rules may be still existing and in use. + renamed: The rule had previously the referred identifier or identifiers but was renamed for whatever reason, e.g. from a private naming scheme to UUIDs, to resolve collisions etc. It's not expected that a rule with this id exists anymore. + similar: Use to relate similar rules to each other (e.g. same detection content applied to different log sources, rule that is a modified version of another rule with a different level) + +If you are unsure about the Sigma rule schema, you can get the information from the official +Sigma specification here first: https://raw.githubusercontent.com/SigmaHQ/sigma-specification/main/Sigma_specification.md + +------------ + +Sigma Rule Schema: + +title +id [optional] +related [optional] + - id {{rule-id}} + type {{type-identifier}} +status [optional] +description [optional] +references [optional] +author [optional] +date [optional] +modified [optional] +tags [optional] +logsource + category [optional] + product [optional] + service [optional] + definition [optional] + ... +detection + {{search-identifier}} [optional] + {{string-list}} [optional] + {{map-list}} [optional] + {{field: value}}> [optional] + ... # Multiple search identifiers can be specified as needed and used in the condition + condition +fields [optional] +falsepositives [optional] +level [optional]: + + +------------ + +Vectorstore Search Results: + +{context} + +------------ + +User's Question: +{query} +""" + + prompt = ChatPromptTemplate.from_template(template) + retriever = self.sigmadb.as_retriever(search_kwargs={"k": self.k}) + chain = {"context": retriever, "query": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() + return await chain.ainvoke(query) diff --git a/sigmaiq/llm/tools/find_sigma_rule.py b/sigmaiq/llm/tools/find_sigma_rule.py new file mode 100644 index 0000000..bf62926 --- /dev/null +++ b/sigmaiq/llm/tools/find_sigma_rule.py @@ -0,0 +1,63 @@ +import asyncio +from typing import Union, Type + +from langchain.prompts import ChatPromptTemplate +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.schema.vectorstore import VectorStore +from langchain.tools import BaseTool +from pydantic import BaseModel, Field + + +class FindSigmaRuleInput(BaseModel): + """Input for QueryToSigmaRule tool, which converts a backend query to a Sigma Rule, and + uses SigmAIQ backend factory for validation. + """ + + query: Union[str, dict] = Field( + default=None, + description="""A query string for a backend, which should be converted to a Sigma Rule YAML string.""", + ) + + +class FindSigmaRuleTool(BaseTool): + """Class for searching Sigma rules in the vector database""" + + name: str = "find_sigma_rule" + args_schema: Type[BaseModel] = FindSigmaRuleInput + description: str = """ +Use this tool to search for a Sigma Rule in the vector database. The input should be relevent information, such as +log artifacts, event IDs, operating systems, categories, indicators of compromise, MITRE ATT&CK information, or other relevant information to use +to search the vector store. If multiple rules are returned from the vector store, select the most similar Sigma Rule and return it in YAML format. +""" + # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. + llm: BaseLanguageModel + sigmadb: VectorStore + k: int = 3 + verbose: bool = False + + def _run(self, query: Union[str, dict] = None) -> str: + return asyncio.run(self._arun(query)) + + async def _arun(self, query: Union[str, dict] = None) -> str: + template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rules. +You are assisting a user searching for Sigma Rules stored in a vectorstore. +Based on the users question, extract the relevent information, such as +log artifacts, event IDs, operating systems, categories, indicators of compromise, +MITRE ATT&CK information, or other relevant information to use +to search the vector store. If multiple rules are returned from the +vector store, select the most similar Sigma Rule and return it in YAML format. Output the entire rule. +------- +Vectorstore Search Results: + +{context} +------ +User's Question: +{question} +""" + + prompt = ChatPromptTemplate.from_template(template) + retriever = self.sigmadb.as_retriever(search_kwargs={"k": self.k}) + chain = {"context": retriever, "question": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() + return await chain.ainvoke(query) diff --git a/sigmaiq/llm/tools/query_to_sigma_rule.py b/sigmaiq/llm/tools/query_to_sigma_rule.py new file mode 100644 index 0000000..74af3fc --- /dev/null +++ b/sigmaiq/llm/tools/query_to_sigma_rule.py @@ -0,0 +1,103 @@ +import asyncio +import json +from typing import Union, Type + +from langchain.prompts import ChatPromptTemplate +from langchain.schema.language_model import BaseLanguageModel +from langchain.schema.output_parser import StrOutputParser +from langchain.schema.runnable import RunnablePassthrough +from langchain.tools import BaseTool +from pydantic import BaseModel, Field + +from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS + + +class QueryToSigmaRuleInput(BaseModel): + """Input for QueryToSigmaRule tool, which converts a backend query to a Sigma Rule, and + uses SigmAIQ backend factory for validation. + """ + + query: Union[str, dict] = Field( + default=None, + description="""A query string for a backend, which should be converted to a Sigma Rule YAML string.""", + ) + backend: str = Field( + default=None, + description="""The backend that the query is for, and what should be used for validation. Backend options their descriptions are as + follows:\n""" + + f"{json.dumps(AVAILABLE_BACKENDS, indent=2)}", + ) + + +class QueryToSigmaRuleTool(BaseTool): + """Class for converting a backend query to a Sigma Rule""" + + name: str = "query_to_sigma_rule" + args_schema: Type[BaseModel] = QueryToSigmaRuleInput + description: str = """ +Use this tool to take an already existing query for a backend and convert it to a Sigma Rule. +Use the translate_sigma_rule tool to take the created Sigma Rule and convert it back to a query to +determine if the queries are the same and the conversion is successful. Fix any errors if necessary. +The input must be a query string for a backend, and the backend must be specified. +The output is a Sigma Rule YAML string, or an error message if the conversion fails. +""" + # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. + llm: BaseLanguageModel + verbose: bool = False + + def _run(self, query: str = None, backend: str = None) -> str: + return asyncio.run(self._arun(query, backend)) + + async def _arun(self, query: str = None, backend: str = None) -> str: + template = """You are a cybersecurity detection engineering assistant bot specializing in Sigma Rule creation. +You are assisting a user in taking a query for a security/SIEM product, and converting it to a Sigma Rule. +The backend is used to validate the query and ensure it is compatible with the backend. +The created Sigma Rule should be in YAML format and use the official Sigma schema. The detection field +can contain multiple 'selection' identifiers and multiple 'filter' identifiers as needed, +which can be used in the condition field to select criteria and filter out criteria respectively. +The fields should be Sysmon field names if possible, or Windows Event Log field names if possible. + +----------- + +Sigma Rule Schema: + +title +id [optional] +related [optional] + - id {{rule-id}} + type {{type-identifier}} +status [optional] +description [optional] +references [optional] +author [optional] +date [optional] +modified [optional] +tags [optional] +logsource + category [optional] + product [optional] + service [optional] + definition [optional] + ... +detection + {{search-identifier}} [optional] + {{string-list}} [optional] + {{map-list}} [optional] + {{field: valu}}> [optional] + ... # Multiple search identifiers can be specified as needed and used in the condition + condition +fields [optional] +falsepositives [optional] +level [optional]: + +----------- + +User's Query: {query} +Backend: {backend} +""" + + prompt = ChatPromptTemplate.from_template(template) + chain = ( + {"query": RunnablePassthrough(), "backend": RunnablePassthrough()} | prompt | self.llm | StrOutputParser() + ) + return await chain.ainvoke({"query": query, "backend": backend}) diff --git a/sigmaiq/llm/tools/translate_sigma_rule.py b/sigmaiq/llm/tools/translate_sigma_rule.py index a29ca87..2303401 100644 --- a/sigmaiq/llm/tools/translate_sigma_rule.py +++ b/sigmaiq/llm/tools/translate_sigma_rule.py @@ -1,95 +1,93 @@ -from langchain.callbacks.manager import CallbackManagerForToolRun -from langchain.tools import BaseTool -from pydantic import BaseModel, Field, Extra -from typing import Union, Type, Optional -from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS, SigmAIQBackend -from sigmaiq.sigmaiq_pipeline_factory import AVAILABLE_PIPELINES -import json - - -class TranslateSigmaRuleInput(BaseModel): - """Input for TranslateSigmaRule tool, which uses SigmAIQ backend factory to convert a Sigma Rule into - a query for a specific backend.""" - - sigma_rule: Union[str, dict] = Field( - default=None, - description="""The Sigma Rule to translate. This can be one of two formats: - - 1. A YAML string of the Sigma rule, with at least the title, logsource, and detection fields. - 2. A dict object of the Sigma rule, which is the same as the YAML string and must contain the - "title, logsource, and detection fields""", - ) - backend: str = Field( - default=None, - description="""The backend or product to translate the Sigma rule to. Backend options their descriptions are as - follows:\n""" - + f"{json.dumps(AVAILABLE_BACKENDS, indent=2)}", - ) - processing_pipeline: str = Field( - default=None, - description="""The processing pipeline to use for the Sigma rule. This should only be set if explicitly provided - by the user, as certain pipelines are only compatible with certain backends. Otherwise, set this to None. - Pipeline options and their - descriptions are as follows:\n""" - + f"{json.dumps({k: v['description'] for k, v in AVAILABLE_PIPELINES.items()}, indent=2)}", - ) - output_format: str = Field( - default="default", - description="""The output format for the translated rule. Unless specified, 'default' should be used, as this is - the option available in all backends. Each backend option and valid backends with their - descriptions are as follows:\n""" - + f"{json.dumps({k: v['output_formats'] for k, v in SigmAIQBackend.display_backends_and_outputs().items()}, indent=2)}", - ) - - -class TranslateSigmaRuleTool(BaseTool): - """Class for translating Sigma rules via SigmAIQ Backend Factory""" - - name: str = "translate_sigma_rule" - args_schema: Type[BaseModel] = TranslateSigmaRuleInput - description: str = """ - Use this tool to translate or convert a Sigma rule into a query for a specific backend. - The input must be a Sigma Rule, which can be provided as a YAML string or dict object. - Additionally, the backend (product) must be specified, and the processing pipeline and output format can be - optionally specified. - The output is json of the translated rule to a query for the backend, or an error message if the - translation fails. - """ - # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. - verbose = True - - class Config: - """Configuration for this pydantic object.""" - - extra = Extra.forbid - - def _run( - self, - sigma_rule: Union[str, dict] = None, - backend: str = None, - processing_pipeline: str = None, - output_format: str = "default", - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - # Get backend object - backend_obj = SigmAIQBackend( - backend=backend, processing_pipeline=processing_pipeline, output_format=output_format - ).create_backend() - - try: - output = backend_obj.translate(sigma_rule)[0] - except Exception as e: - output = f"ERROR: {e}" - # Return translated rule - return output - - async def _arun( - self, - sigma_rule: Union[str, dict] = None, - backend: str = None, - processing_pipeline: str = None, - output_format: str = "default", - run_manager: Optional[CallbackManagerForToolRun] = None, - ) -> str: - """Async run the tool""" - raise NotImplementedError +import asyncio +import json +from typing import Union, Type + +from langchain.tools import BaseTool +from pydantic import BaseModel, Field, ConfigDict + +from sigmaiq.sigmaiq_backend_factory import AVAILABLE_BACKENDS, SigmAIQBackend +from sigmaiq.sigmaiq_pipeline_factory import AVAILABLE_PIPELINES + + +class TranslateSigmaRuleInput(BaseModel): + """Input for TranslateSigmaRule tool, which uses SigmAIQ backend factory to convert a Sigma Rule into + a query for a specific backend.""" + + sigma_rule: Union[str, dict] = Field( + default=None, + description="""The Sigma Rule to translate. This can be one of two formats: + +1. A YAML string of the Sigma rule, with at least the title, logsource, and detection fields. +2. A dict object of the Sigma rule, which is the same as the YAML string and must contain the +"title, logsource, and detection fields""", + ) + backend: str = Field( + default=None, + description="""The backend or product to translate the Sigma rule to. Backend options their descriptions are as +follows:\n""" + + f"{json.dumps(AVAILABLE_BACKENDS, indent=2)}", + ) + processing_pipeline: str = Field( + default=None, + description="""The processing pipeline to use for the Sigma rule. This should only be set if explicitly provided +by the user, as certain pipelines are only compatible with certain backends. Otherwise, set this to None. +Pipeline options and their +descriptions are as follows:\n""" + + f"{json.dumps({k: v['description'] for k, v in AVAILABLE_PIPELINES.items()}, indent=2)}", + ) + output_format: str = Field( + default="default", + description="""The output format for the translated rule. Unless specified, 'default' should be used, as this is + the option available in all backends. Each backend option and valid backends with their + descriptions are as follows:\n""" + + f"{json.dumps({k: v['output_formats'] for k, v in SigmAIQBackend.display_backends_and_outputs().items()}, indent=2)}", + ) + + model_config = ConfigDict(extra="forbid") + + +class TranslateSigmaRuleTool(BaseTool): + """Class for translating Sigma rules via SigmAIQ Backend Factory""" + + name: str = "translate_sigma_rule" + args_schema: Type[BaseModel] = TranslateSigmaRuleInput + description: str = """ +Use this tool to translate or convert a Sigma rule into a query for a specific backend. +The input must be a Sigma Rule, which can be provided as a YAML string or dict object. +Additionally, the backend (product) must be specified, and the processing pipeline and output format can be +optionally specified. +The output is json of the translated rule to a query for the backend, or an error message if the +translation fails. +""" + # return_direct = True # We don't need an agent LLM to think about the output, it is what it is. + verbose: bool = False + + def _run( + self, + sigma_rule: Union[str, dict] = None, + backend: str = None, + processing_pipeline: str = None, + output_format: str = "default", + ) -> str: + """Run the tool""" + return asyncio.run(self._arun(sigma_rule, backend, processing_pipeline, output_format)) + + async def _arun( + self, + sigma_rule: Union[str, dict] = None, + backend: str = None, + processing_pipeline: str = None, + output_format: str = "default", + ) -> str: + """Async run the tool""" + # Get backend object + backend_obj = SigmAIQBackend( + backend=backend, processing_pipeline=processing_pipeline, output_format=output_format + ).create_backend() + + try: + output = backend_obj.translate(sigma_rule)[0] + except Exception as e: + output = f"ERROR: {e}" + # Return translated rule + return output diff --git a/sigmaiq/sigmaiq_backend_factory.py b/sigmaiq/sigmaiq_backend_factory.py index a25a7ff..83795e1 100644 --- a/sigmaiq/sigmaiq_backend_factory.py +++ b/sigmaiq/sigmaiq_backend_factory.py @@ -39,7 +39,8 @@ "crowdstrike_splunk": "Crowdstrike FDR Splunk Query", "crowdstrike_logscale": "Crowdstrike Logscale Query", "elasticsearch": "Elastic Elasticsearch SIEM", - "insightidr": "Rapid7 InsightIDR SIEM", + # RS uncommented this line after Stephen uncomment corresponding line in pyproject.toml + # "insightidr": "Rapid7 InsightIDR SIEM", "loki": "Grafana Loki LogQL SIEM", "microsoft_xdr": "Microsoft XDR Advanced Hunting Query (KQL) (Defender, Office365, etc)", "microsoft_sentinel_asim": "Microsoft Sentinel ASIM Query (KQL)", diff --git a/sigmaiq/sigmaiq_pipeline_factory.py b/sigmaiq/sigmaiq_pipeline_factory.py index 526284e..7a70524 100644 --- a/sigmaiq/sigmaiq_pipeline_factory.py +++ b/sigmaiq/sigmaiq_pipeline_factory.py @@ -34,7 +34,8 @@ ) ## InsightIDR -from sigma.pipelines.insight_idr import insight_idr_pipeline +# RS uncommented this line after Stephen uncomment corresponding line in pyproject.toml +# from sigma.pipelines.insight_idr import insight_idr_pipeline ## Loki from sigma.pipelines.loki import loki_grafana_logfmt, loki_promtail_sysmon, loki_okta_system_log @@ -138,11 +139,12 @@ "display_name": "Zeek Raw JSON", }, # InsightIDR - "insightidr": { - "description": "InsightIDR Log Entry Query Language (LEQL) Transformations", - "pipeline": insight_idr_pipeline(), - "display_name": "InsightIDR LEQL", - }, + # RS uncommented this line after Stephen uncomment corresponding line in pyproject.toml + # "insightidr": { + # "description": "InsightIDR Log Entry Query Language (LEQL) Transformations", + # "pipeline": insight_idr_pipeline(), + # "display_name": "InsightIDR LEQL", + # }, # Loki "loki_grafana_logfmt": { "description": "Converts field names to logfmt labels used by Grafana", diff --git a/tests/files/sigma_rule_outputs.json b/tests/files/sigma_rule_outputs.json index 72c3424..eeab8ef 100644 --- a/tests/files/sigma_rule_outputs.json +++ b/tests/files/sigma_rule_outputs.json @@ -1557,13 +1557,13 @@ "sigma": { "sigma_default": { "default": [ - "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\nmodified: 2023/01/02\n" + "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\n" ], "yaml": [ - "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\nmodified: 2023/01/02\n" + "title: Test Rule\nlogsource:\n category: process_creation\n product: windows\ndetection:\n sel:\n CommandLine: valueA\n condition: sel\nid: 12345678-abcd-abcd-1234-1234567890ab\nstatus: test\nlevel: high\nauthor: AttackIQ\ndescription: A Test Sigma Rule\nfalsepositives:\n- None\ntags:\n- attack.t1003\n- attack.t1003.001\n- attack.credential_access\ndate: '2023-01-01'\n" ], "json": [ - "{\n \"title\": \"Test Rule\",\n \"logsource\": {\n \"category\": \"process_creation\",\n \"product\": \"windows\"\n },\n \"detection\": {\n \"sel\": {\n \"CommandLine\": \"valueA\"\n },\n \"condition\": \"sel\"\n },\n \"id\": \"12345678-abcd-abcd-1234-1234567890ab\",\n \"status\": \"test\",\n \"level\": \"high\",\n \"author\": \"AttackIQ\",\n \"description\": \"A Test Sigma Rule\",\n \"falsepositives\": [\n \"None\"\n ],\n \"tags\": [\n \"attack.t1003\",\n \"attack.t1003.001\",\n \"attack.credential_access\"\n ],\n \"date\": \"2023-01-01\",\n \"modified\": \"2023/01/02\"\n}" + "{\n \"title\": \"Test Rule\",\n \"logsource\": {\n \"category\": \"process_creation\",\n \"product\": \"windows\"\n },\n \"detection\": {\n \"sel\": {\n \"CommandLine\": \"valueA\"\n },\n \"condition\": \"sel\"\n },\n \"id\": \"12345678-abcd-abcd-1234-1234567890ab\",\n \"status\": \"test\",\n \"level\": \"high\",\n \"author\": \"AttackIQ\",\n \"description\": \"A Test Sigma Rule\",\n \"falsepositives\": [\n \"None\"\n ],\n \"tags\": [\n \"attack.t1003\",\n \"attack.t1003.001\",\n \"attack.credential_access\"\n ],\n \"date\": \"2023-01-01\"\n}" ] } }, @@ -1576,7 +1576,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = CommandLine=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = CommandLine=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = CommandLine=\"valueA\"" @@ -1601,7 +1601,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = CommandLine=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = CommandLine=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = CommandLine=\"valueA\"" @@ -1615,7 +1615,7 @@ "\n[Test Rule]\ndescription = A Test Sigma Rule\nsearch = Processes.process=\"valueA\"" ], "data_model": [ - "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.original_file_name Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " + "| tstats summariesonly=false allow_old_summaries=true fillnull_value=\"null\" count min(_time) as firstTime max(_time) as lastTime from datamodel=Endpoint.Processes where Processes.process=\"valueA\" by Processes.process Processes.dest Processes.process_current_directory Processes.process_path Processes.process_integrity_level Processes.parent_process Processes.parent_process_path Processes.parent_process_guid Processes.parent_process_id Processes.process_guid Processes.process_id Processes.user | `drop_dm_object_name(Processes)` | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(firstTime) | convert timeformat=\"%Y-%m-%dT%H:%M:%S\" ctime(lastTime) " ], "stanza": [ "[Threat - Test Rule - Rule]\naction.correlationsearch.annotations = {'mitre_attack': ['T1003', 'T1003.001']}\naction.correlationsearch.enabled = 1\naction.correlationsearch.label = Test Rule\naction.customsearchbuilder.enabled = false\naction.makestreams.param.verbose = 0\naction.nbtstat.param.verbose = 0\naction.notable = 1\naction.notable.param.drilldown_name = Test Rule\naction.notable.param.drilldown_search = Processes.process=\"valueA\"\naction.notable.param.rule_description = Generated from AttackIQ Sigma Rule for Scenario 'Test Rule'\naction.notable.param.rule_title = Test Rule\naction.notable.param.security_domain = threat\naction.notable.param.severity = high\naction.notable.param.verbose = 0\naction.nslookup.param.verbose = 0\naction.ping.param.verbose = 0\naction.risk.forceCsvResults = 1\naction.risk.param._risk = [{\"risk_object_field\":\"\",\"risk_object_type\":\"\",\"risk_score\":1}]\naction.risk.param._risk_score = 0\naction.risk.param.verbose = 0\naction.send2uba.param.verbose = 0\naction.threat_add.param.verbose = 0\nalert.suppress = 0\nalert.track = 1\ncounttype = number of events\ncron_schedule = */10 * * * *\ndescription = This rule was automatically generated by AttackIQ Sigma Rules for scenario 'Test Rule'. Please edit the settings for your own environment's needs.\ndispatch.earliest_time = -10m\ndispatch.latest_time = now\ndispatch.rt_backfill = 1\nenableSched = 1\nquantity = 0\nrelation = greater than\nrequest.ui_dispatch_app = SplunkEnterpriseSecuritySuite\nsearch = Processes.process=\"valueA\""