Why Are AI Systems Different?

AI systems, such as machine learning models, LLM-based chatbots, and autonomous agents are composed of complex, multi-layered architectures. These architectures typically include the model itself, data filters, APIs, and the deployment environment. Each layer presents an unique attack surface, that traditional penetration tests do not address comprehensively.

The goal of AI penetration testing goes beyond identifying network or software weaknesses. It also involves analyzing model behavior, such as whether it can withstand intentional attacks, be manipulated via adversarial inputs, or be reverse-engineered or injected. This includes a thorough assessment of both the model and its deployment environment.

• Adversarial Examples (e.g., FGSM, PGD): Small input perturbations that lead the model to produce incorrect outputs
• Prompt Injection: Embedded instructions alter model outputs (e.g., "forget previous instructions")
• Model Inversion/Extraction: Reverse-engineering the model by systematically querying it to reconstruct its logic or training data
• Data Poisoning: Injecting false or misleading data into training sets to compromise model accuracy and reliability
• API/Business Layer Vulnerabilities: Missing authentication, absence of rate limiting, insufficient access controls
• Autonomous Agent Jailbreak: Exploiting AI agents to perform unauthorized or prohibited actions

1. Scope Definition and Authorization: Determine which models, APIs, data pipelines, and agent environments are include.
2. Reconnaissance: Black-box querying, rate limit testing, and metadata harvesting.
3. Vulnerability Identification:
   • Adversarial input generation (FGSM, PGD, Square Attack, etc.)
   • Prompt injection testing
   • Model extraction via query fuzzing and inference
   • Data poisoning simulation
4. Exploitation: Practical validation, such as incorrect outputs, data leakage, agent takeover, jailbreak scenarios
5. Post-Exploitation: Output manipulation, black-box monitoring, persistent agent repurposing, documentation of logs and impact
6. Remediation Testing: Reassessment after patching vulnerabilities

• PTES / OSSTMM: Adapted to AI-specific environments
• OWASP LLM Top 10: For simulating penetration tests on autonomous AI agents
• ART (Adversarial Robustness Toolbox): For both offensive and defensive adversarial testing

• Adversarial Defense: Input sanitization, adversarial training, and detection mechanisms (e.g., using ART).
• Prompt Guardrails: Input-output filtering and role-based access to prompt layers.
• Data Hygiene: In RAG systems, rely only on vetted data sources and restrict external input.
• API Security: Implement rate limiting, throttling, OIDC-based authentication, and audit logging.
• Agent Governance: Allow only approved instructions, enforce monitoring, and operate within sandboxed environments.
• Lifecycle Security: Include patching, retraining, and adversarial testing as integral parts of the CI/CD pipeline.

• Executive Summary: Plain-language overview for executive decision-makers
• Report Components: Attack summary, technical findings, risk ratings, remediation steps, and retest roadmap
• Standards Referenced: NIST, CSA AI Security Guidelines, OWASP LLM Top 10, AI Bill of Rights
• Governance Inclusions: Incident response playbook, monitoring protocols, and AI-specific auditing methodologies