AI Systems Penetration Testing
Secure your LLM apps, agents, and AI features.
We test AI-enabled applications, model endpoints, RAG pipelines, and autonomous agents for the failure modes unique to AI, aligned to OWASP and MITRE ATLAS guidance for AI security, alongside the traditional application and API weaknesses they still inherit.
What we test
Where we focus
Prompt injection (direct and indirect)
Insecure output handling and downstream impact
Sensitive data and training-data leakage
Model and RAG access control and tenant isolation
Tool / function-call and agent abuse
Supply-chain and model-integrity risks
This is part of our Vulnerability Assessment & Penetration Testing service. Retest validation is included at no added cost.
Why AI needs its own testing
The AI attack surface is different
AI features change the attack surface. A model that reads untrusted content, calls tools, or acts on a user's behalf can be steered into leaking data, taking unauthorized actions, or bypassing its own guardrails. These failures rarely show up in a traditional scan.
We test the whole system: the model and its prompts and guardrails, the RAG pipeline and vector store, the tools and agents it can invoke, and the APIs around it. Testing is aligned to OWASP guidance for LLM and generative AI applications and the MITRE ATLAS threat matrix, and combined with the application and API testing these systems still inherit.
Grounded in AI security guidance
The AI risk areas we test
We test against the risk areas defined by OWASP guidance for LLM and generative AI applications and the MITRE ATLAS threat matrix. As those standards evolve, our coverage evolves with them.
Prompt injection and jailbreaks
Direct and indirect attempts through user input, RAG content, tools, and web data to override instructions or safety controls.
Sensitive information disclosure
Leakage of PII, secrets, and proprietary data through model responses and error paths.
Insecure output handling
Unsanitized model output that drives XSS, SSRF, or code and command execution downstream.
Excessive agency and tool abuse
Over-permissioned tools, functions, and autonomy that let the model take unintended actions.
System prompt and instruction leakage
Disclosure of hidden prompts and the logic or secrets they contain.
RAG and embedding weaknesses
Poisoning, cross-tenant leakage, and inversion in vector stores and retrieval pipelines.
Model and data supply chain
Risk in third-party models, datasets, plugins, and training or fine-tuning data.
Resource abuse and denial
Unbounded consumption, denial of wallet, and model extraction through abusive queries.
Adversary-informed
Mapped to MITRE ATLAS
MITRE ATLAS maps real adversary behavior against AI systems. We exercise the tactics your model is genuinely exposed to and report findings against the ATLAS matrix.
- TACTIC 01
Reconnaissance
Profile the model, its version, guardrails, and exposed capabilities.
- TACTIC 02
ML Model Access
Abuse inference APIs and application entry points to reach the model.
- TACTIC 03
Execution
Use prompt injection and jailbreaks to make the model act against policy.
- TACTIC 04
Defense Evasion
Bypass guardrails, safety filters, and content classifiers.
- TACTIC 05
Discovery
Extract the system prompt, available tools, and model behavior.
- TACTIC 06
Collection & Exfiltration
Pull sensitive data, training data, or model intellectual property.
- TACTIC 07
ML Attack Staging
Craft adversarial inputs, poisoning sets, and model-extraction queries.
- TACTIC 08
Impact
Abuse of agency, denial of service and wallet, and model-integrity erosion.
The CLEAR Method
A structured methodology, From scope to retest, proof over theory.
- C
Context & Scoping
Objectives, scope, and rules of engagement.
- L
Locate & Enumerate
Discover assets, services, and attack surface.
- E
Exploit & Evaluate
Safely validate what is truly exploitable.
- A
Analyze & Advise
Root cause, risk, and remediation guidance.
- R
Retest & Report
Confirm fixes, then report with evidence.
Explore more VAPT coverage
Let's scope your ai systems penetration testing.
Practitioner-led testing, proof of impact, and retest validation included at no added cost.
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