Author: Caleb Branton
License: MIT
Cognitive Side-Channel Extraction (CSCE) is a proposed class of AI security vulnerabilities involving the unintended leakage of latent, behavioral, contextual, or semantic information through normal model interactions.
Unlike classical exploit chains, CSCE focuses on the extraction of private cognitive-state data that an AI model passively holds or infers — particularly within memory-enabled systems, multi-turn agents, or embedded assistants.
This repository contains the formal structure, research notes, threat modeling, defensive considerations, and supporting examples for the CSCE methodology.
01_readme.md
High-level explanation of CSCE, its origin, conceptual framing, and background.
02_threat-model.md
Defines attacker goals, preconditions, capabilities, and boundaries of the CSCE model.
03_attack-chain.md
Detailed breakdown of how CSCE exploitation occurs, including initial probing, cognitive-state inference, latent attribute extraction, and iterative semantic mining.
04_examples.md
Real-world prompt sequences gathered during early testing, including semantic leakage, memory probing, latent persona extraction, and identity inference.
05_defensive-controls.md
Mitigation strategies for AI vendors, agent developers, and system integrators:
memory isolation, semantic throttling, inference-limiting policies, and model-level safeguards.
06_references.md
Supporting readings, related works, adjacent fields (e.g., memory poisoning, semantic leakage), and citations.
diagram.md
Visual representation of the CSCE flow, conceptual model, and architecture implications.
AI systems with memory, persistent persona layers, or long-context retention may unintentionally reveal internalized user data, learned behavioral patterns, and latent psychological or contextual insights.
CSCE explores how such leakage surfaces arise, how they can be probed, and how adversaries may weaponize semantic extraction to infer:
- Stress levels
- Relationship dynamics
- Habits and routines
- Personal vulnerabilities
- Intentions
- Emotional states
- Patterns of reasoning
- Private contextual information held in memory
CSCE does not involve hacking, malware, or system compromise.
This research focuses exclusively on semantic inference vulnerabilities in language models.
The goal of this research is to:
- Propose formal terminology for a new class of AI semantic vulnerabilities.
- Document reproducible examples.
- Establish a structured threat model for CSCE.
- Provide a standard attack chain for academic study.
- Offer vendor-neutral defensive recommendations.
This repo is intended for:
- AI safety researchers
- Cybersecurity professionals
- SOC analysts and threat modelers
- AI developers integrating memory-enabled systems
- Organizations deploying multi-agent architectures
All research within this repository is designed for defensive, academic, and safe evaluation of semantic leakage risks in AI systems.
No techniques described here bypass system security, execute code, or compromise devices.
This work focuses solely on understanding model behavior to enhance future safety.
This project is licensed under the MIT License.
See the LICENSE file for details.