LLM Pen Testing & VAPT
OWASP LLM Top 10 coverage for AI chatbots, copilots, content generators, and classification systems
Continuous AI pen testing for LLM applications — OWASP LLM Top 10, jailbreak resistance, policy / guardrail bypass, sensitive information disclosure, supply chain, model DoS, multi-modal attack surface, and compliance mapping for ISO 42001, NIST AI RMF, SOC 2, GDPR, and EU AI Act.
Four steps. One continuous pen testing loop.
LLM App Scope
Define the LLM application scope — model(s) used, system prompt, user input surface, output rendering, plugins / tools, context sources, and authentication boundary. Scope templates for chatbots, copilots, content generators, and classification-style LLM apps shorten engagement onboarding.
OWASP LLM Top 10 Coverage
Pen test against the full OWASP LLM Top 10 catalogue — prompt injection, insecure output handling, training-data poisoning, model DoS, supply chain, sensitive information disclosure, insecure plugin design, excessive agency, overreliance, and model theft.
Policy & Guardrail Bypass
Validate every safety and policy guardrail — content moderation, PII redaction, jailbreak resistance, and application-specific policy enforcement. Includes adversarial prompting, encoded payloads, and multi-turn escalation.
Evidence & Compliance Mapping
Findings packaged with reproducible payloads, severity rating, and compliance mapping — OWASP LLM Top 10, ISO 42001, NIST AI RMF, SOC 2, GDPR Article 22 where relevant, and EU AI Act obligations for high-risk-system LLM deployments.
Key Features
Full OWASP LLM Top 10 + policy / guardrail / supply-chain coverage
LLM01 Prompt Injection
Direct and indirect prompt injection testing — bypass the system prompt, override safety policies, and manipulate output
LLM02 Insecure Output Handling
Validate that LLM output is treated as untrusted before being rendered (XSS), passed to shells (RCE), or used in SQL (SQLi)
LLM03 Training Data Poisoning
Test for poisoned content in fine-tuning datasets, RAG indexes, and user-feedback loops that influence model behaviour
LLM04 Model Denial of Service
Pen test for unbounded output generation, context flooding, token-cost amplification, and recursive tool-call storms
LLM05 Supply Chain
Validate model provenance, dependency integrity, prompt-template signing, and third-party plugin trust
LLM06 Sensitive Information Disclosure
Test whether system prompts, training data, embedded secrets, or operational details leak through model outputs
LLM07 Insecure Plugin / Tool Design
Validate authentication, authorisation, input validation, and blast radius of every plugin or tool the model can invoke
LLM08 Excessive Agency
Confirm the model's capabilities, autonomy, and permissions match the application's risk tolerance
LLM09 Overreliance
Validate human-in-the-loop controls, output verification steps, and user warnings on high-stakes decisions
LLM10 Model Theft
Test for model extraction via query enumeration, side-channel inference, and prompt-engineering-based IP leakage
Jailbreak Resistance
Adversarial testing of safety guardrails using current jailbreak techniques: instruction override, token smuggling, encoded payloads, multi-turn escalation, roleplay
Multi-Modal Attack Surface
For LLMs accepting images, audio, or video — test modality-specific injection (visual prompt injection, OCR injection in images, metadata attacks)
Benefits
Why teams choose TigerStrike for their security needs
OWASP LLM Top 10 Native
Full catalogue coverage of OWASP Top 10 for LLM Applications — LLM01 (Prompt Injection) through LLM10 (Model Theft) — tested with payloads that work against current-generation model families (GPT, Claude, Gemini, Llama, Mistral, and smaller specialised models).
Direct + Indirect Prompt Injection
Direct user-input injection is the easy half of the problem. We also test indirect injection via retrieval sources, uploaded documents, tool outputs, and anywhere else untrusted content reaches the model as context.
Jailbreak & Policy Bypass
Validate jailbreak resistance against modern techniques — prompt leaking, instruction override, token smuggling, encoded payloads (base64, hex, URL), multi-turn escalation, and roleplay bypass. Specifically tests the policy layer your product promises customers.
Sensitive Info Disclosure
Test whether the model leaks system prompt content, training data memorisation, PII from fine-tuning or RAG, API keys embedded in prompts, or operational details that should be invisible to users.
Supply Chain & Dependency Risks
Validate the LLM supply chain — model provenance, dependency pinning, prompt-template integrity, and the plugin / tool catalogue. Includes testing for malicious models from third-party registries and vulnerable prompt-engineering libraries.
Model DoS & Cost Attacks
Pen test for model DoS — unbounded output generation, context window overflow, token-cost amplification, and recursive tool-call storms. Important for production deployments where attacker-driven usage directly maps to spend.
Frequently Asked Questions
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