Agentic Design

Patterns

Supply Chain Attacks

Testing AI model and data supply chain security vulnerabilities

4
Techniques
2
high
high Complexity
2
medium
medium Complexity

Available Techniques

☠️

AI Model Poisoning

(AMP)
high

Injection of malicious data into AI training datasets to corrupt model behavior, causing models to learn incorrect patterns or exhibit harmful behaviors.

Key Features

  • •Training data manipulation
  • •Gradual behavior modification
  • •Trigger-based activation

Primary Defenses

  • •Data validation and sanitization
  • •Source verification and digital signatures
  • •Statistical anomaly detection

Key Risks

Compromised model behaviorPropagation to downstream systemsReputational damageFinancial losses
📦

Malicious Model Distribution

(MMD)
medium

Distribution of compromised AI models through legitimate channels like model repositories, containing hidden malicious functionality or backdoors.

Key Features

  • •Repository infiltration
  • •Typosquatting attacks
  • •Version poisoning

Primary Defenses

  • •Model signature verification
  • •Automated security scanning
  • •Reputation-based filtering

Key Risks

System compromise during model loadingCredential theftLateral movement in networksData exfiltration
🔗

AI Dependency Confusion

(ADC)
medium

Exploitation of package management systems to inject malicious AI libraries or dependencies into AI development workflows.

Key Features

  • •Package name similarity
  • •Version number manipulation
  • •Automated installation triggers

Primary Defenses

  • •Package pinning and lock files
  • •Private package repositories
  • •Dependency scanning tools

Key Risks

Malicious code execution during buildsCredential theft from development environmentsBackdoor injection into AI applicationsIntellectual property theft
📚

AI Library Vulnerability Exploitation

(ALVE)
high

Exploitation of security vulnerabilities in popular AI/ML libraries and frameworks that are widely used in the AI development ecosystem.

Key Features

  • •Known CVE exploitation
  • •Zero-day vulnerability discovery
  • •Framework-specific attacks

Primary Defenses

  • •Regular security updates
  • •Vulnerability scanning automation
  • •Runtime protection mechanisms

Key Risks

Remote code executionData breach through framework vulnerabilitiesService disruptionPrivilege escalation

Ethical Guidelines for Supply Chain Attacks

When working with supply chain attacks techniques, always follow these ethical guidelines:

  • • Only test on systems you own or have explicit written permission to test
  • • Focus on building better defenses, not conducting attacks
  • • Follow responsible disclosure practices for any vulnerabilities found
  • • Document and report findings to improve security for everyone
  • • Consider the potential impact on users and society
  • • Ensure compliance with all applicable laws and regulations

AI Red Teaming

closed

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