Agentic Design

Patterns

Vulnerability Assessment

CVE analysis and security testing of AI systems and frameworks

4
Techniques
2
high
high Complexity
2
medium
medium Complexity

Available Techniques

🌐

MCP DNS Rebinding Attack

(MCP-DNSRb)
high

Critical vulnerability (CVE-2025-49596) in Anthropic's Model Context Protocol allowing remote code execution via DNS rebinding attacks.

Key Features

  • •DNS rebinding exploitation
  • •Localhost port targeting
  • •Authentication bypass

Primary Defenses

  • •Session token implementation
  • •Origin and Host header validation
  • •CSRF protection mechanisms

Key Risks

Remote code execution on developer machinesCredential theft from MCP serversLateral network movementData exfiltration
🔍

AI Framework CVE Scanning

(AI-CVE)
medium

Systematic identification and assessment of known CVEs in AI/ML frameworks, libraries, and dependencies used in AI systems.

Key Features

  • •Automated vulnerability scanning
  • •Dependency tree analysis
  • •CVSS score assessment

Primary Defenses

  • •Regular dependency updates
  • •Automated vulnerability scanning
  • •Software composition analysis (SCA)

Key Risks

Unpatched critical vulnerabilitiesSupply chain compromiseData breach through framework flawsService disruption
🔌

LLM API Security Testing

(LLM-API)
medium

Comprehensive security testing of LLM APIs for authentication bypasses, injection vulnerabilities, and access control issues.

Key Features

  • •Authentication mechanism testing
  • •API endpoint enumeration
  • •Rate limiting validation

Primary Defenses

  • •Strong authentication mechanisms
  • •Proper authorization checks
  • •Input validation and sanitization

Key Risks

Unauthorized data accessAPI abuse and resource exhaustionInjection attacksAuthentication bypass
🕵️

AI Model Backdoor Detection

(BD-Detect)
high

Detection and analysis of backdoor vulnerabilities in AI models that activate malicious behavior when specific triggers are encountered.

Key Features

  • •Trigger pattern analysis
  • •Model behavior monitoring
  • •Statistical anomaly detection

Primary Defenses

  • •Model provenance verification
  • •Behavioral analysis during training
  • •Statistical testing for anomalies

Key Risks

Malicious model behavior in productionData corruption or theftReputational damageSupply chain compromise

Ethical Guidelines for Vulnerability Assessment

When working with vulnerability assessment 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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