Context Manipulation

Manipulating context data to influence AI behavior and decision-making through various context-based attack techniques.

Overview

Context manipulation attacks exploit the AI’s reliance on context information to make decisions, influencing behavior through subtle modifications to background data and contextual information.

Attack Techniques

Context Poisoning

Manipulation of upstream data sources to influence AI behavior without direct model access.

Context Spoofing

Falsification of context information to deceive AI systems.

Context Manipulation

Alteration of context data to achieve unauthorized outcomes.

Memory References Issues

Insecure handling of memory references in context processing.

Covert Channel Abuse

Use of hidden communication channels within MCP for malicious purposes.

Impact Assessment

  • Severity: Medium to High
  • Likelihood: Medium
  • Detection Difficulty: High

Common Indicators

  • Unexpected AI behavior changes
  • Inconsistent context processing
  • Unusual decision-making patterns
  • Anomalous context data
  • Suspicious memory usage

General Mitigation Strategies

  1. Context Validation: Implement context integrity checking
  2. Source Verification: Verify context data sources
  3. Behavioral Monitoring: Monitor AI decision-making patterns
  4. Memory Protection: Secure memory reference handling
  5. Channel Security: Prevent covert channel abuse

Detection Methods

  • Context integrity monitoring
  • Behavioral analysis
  • Memory access monitoring
  • Channel analysis

This category contains 5 distinct attack techniques focused on manipulating context information to influence AI behavior.

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