Cognitive Side-Channel Extraction (CSCE)

An Emerging Post-Compromise Semantic-Intelligence Threat in AI-Assisted Environments

Overview

Cognitive Side-Channel Extraction (CSCE) is a proposed threat category describing how an attacker—after compromising a victim’s endpoint or authenticated session—can interrogate a memory-enabled AI assistant to extract structured, high-value semantic intelligence.

Modern AI assistants often retain summaries of user behavior, preferences, relationships, routines, and operational details. Once a device or session is compromised, this synthesized contextual knowledge becomes an intelligence-rich side-channel available to adversaries.

CSCE is not prompt injection, memory poisoning, or model exfiltration.
It is a post-compromise semantic reconnaissance technique that leverages the assistant’s accumulated understanding of the user.

This repository defines the concept, its threat model, attack sequence, potential impact, and associated defensive strategies.

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What Makes CSCE Distinct

Traditional data exfiltration focuses on raw assets:

• credentials

• documents

• emails

• configuration files

• logs

• source code

CSCE instead targets the assistant’s internal semantic model of the user—a distilled and searchable representation generated through normal interaction.

This model may contain:

• behavioral patterns and routines

• relationship mappings and social context

• personal goals, concerns, and long-term plans

• organizational responsibilities and workflows

• technology usage patterns

• psychological or emotional cues

AI assistants convert fragmented information into coherent summaries.
CSCE exploits this synthesis rather than raw data stores.

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Why CSCE Matters

As AI assistants gain:

• long-term memory,

• deeper context retention,

• cross-platform integration, and

• access to personal or workplace data,

they increasingly function as compressed personal knowledge bases.

During a breach, they act as high-bandwidth intelligence surfaces, enabling attackers to extract meaningfully structured insights far more efficiently than traditional forensic or manual search methods.

CSCE reframes the impact of post-compromise access:

• Old question: “What files were accessed?”

• New question: “What does the assistant know about the user?”

This shift has implications for personal security, enterprise risk, insider-threat modeling, and breach-response workflows.

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Repository Structure

• 01_overview.md — Overview
  Conceptual introduction and high-level framing of CSCE.

• 02_threat-model.md — Threat Model
  Adversary profiles, preconditions, and assets at risk.

• 03_attack-chain.md — Attack Sequence
  Generalized kill-chain describing CSCE exploitation.

• 04_examples.md — Example Scenarios
  Hypothetical cases illustrating CSCE across environments.

• 05_defensive-controls.md — Mitigation Strategies
  Controls for individuals, enterprises, SOC teams, and AI platform designers.

• 06_references.md — Related Work
  Sources describing partially overlapping AI security risks.

• diagram.mmd — Visual Model
  Mermaid diagram depicting the CSCE process.

• license — MIT License
  Open-use licensing for research and derivative work.

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License

This project is provided under the terms of the MIT License.
See license for details.