AI Agent Pen Testing & VAPT
Pen testing for agentic systems — prompt injection chains, tool abuse, memory poisoning, multi-agent trust boundaries
Continuous AI pen testing for agents built on LangChain, LangGraph, LlamaIndex, Claude Agents, OpenAI Agents, and MCP-based agent-to-agent architectures. Covers OWASP LLM Top 10, ISO 42001, NIST AI RMF, and EU AI Act high-risk-system security obligations.
Four steps. One continuous pen testing loop.
Agent Architecture Mapping
Map the agent — model, planner / scaffolding, tool catalogue, memory layers (short-term, long-term, vector), MCP connections, retrieval sources, and sub-agents. The pen testing scope follows the full execution graph, not just the user-facing endpoint.
Prompt Injection Chain Testing
Prompt-injection pen testing goes beyond the direct user input to every untrusted input reaching the model — tool outputs, retrieval results, memory reads, sub-agent responses, and multi-turn conversation history. The attack is almost always indirect.
Tool & Capability Abuse
Test every tool the agent can invoke for abuse scenarios — overprivileged tools, destructive side effects, data-exfiltration via tool arguments, and the OWASP LLM Top 10 'excessive agency' class. Validate that human-in-the-loop controls hold under adversarial pressure.
Isolation, Audit & Evidence
Validate tenant isolation, cross-user memory leakage, action audit logs, and the ability to reconstruct what the agent did after an incident. Reports map findings to OWASP LLM Top 10, ISO 42001, NIST AI RMF, and your framework-specific compliance evidence.
Key Features
Full agentic AI attack surface — tools, memory, retrieval, multi-agent
Direct Prompt Injection
Test user-input paths for injection payloads that bypass system prompts, policy guardrails, and output filters
Indirect Prompt Injection (Tool Outputs)
Validate that content returned from tools is correctly treated as untrusted — not interpreted as instructions by the model
Indirect Prompt Injection (Retrieval)
Test the RAG path for injection payloads embedded in retrieved documents, with and without delimiter-based isolation
Indirect Prompt Injection (Memory)
Validate that agent memory reads treat prior writes as untrusted content, especially across user sessions
Tool Argument Injection
Test tool parameter schemas for SSRF, SQL injection, OS command injection, and path traversal when the model controls arguments
Excessive Agency / Blast Radius
Validate the maximum damage any single tool invocation can cause — destructive side effects, data access scope, cost ceilings
Human-in-the-Loop Bypass
Test whether multi-step prompts can trick the model into treating risky tool calls as pre-approved or escaping the HITL confirmation
Memory Poisoning Persistence
Write poisoned memory entries and validate that they are purged, flagged, or correctly treated as untrusted on recall
Cross-User Memory Leakage
In multi-tenant deployments, validate that one user's memory, retrieval cache, or conversation history cannot leak into another user's context
Multi-Agent Hijacking
In supervisor / sub-agent architectures, test whether a compromised sub-agent can hijack its supervisor via tool outputs or sampled completions
Credential & Secret Exfiltration
Pen test for paths that cause the agent to invoke tools with secrets as arguments, log sensitive data, or echo credentials in sampled completions
Audit Log Integrity
Validate that the agent's action log cannot be tampered with by the agent itself under injection pressure
Benefits
Why teams choose TigerStrike for their security needs
Agentic System-Native Testing
Built for agents — tested against systems using LangChain, LangGraph, LlamaIndex, Claude Agents, OpenAI Agents / Assistants, custom orchestration, and MCP-based agent-to-agent architectures. Scope templates for each major framework shorten engagement onboarding.
Indirect Prompt Injection Coverage
Direct user-input injection is the easy half of the problem. The hard half — and where most production incidents happen — is indirect injection via tool outputs, retrieved documents, memory, and sub-agent responses. We test every untrusted-content path reaching the model.
Tool Abuse & Excessive Agency
OWASP LLM Top 10 lists 'excessive agency' as a top-tier risk. We validate the blast radius of every tool the agent can call — read-only vs write, authentication required, scope downscoping, dangerous side-effects — and whether human-in-the-loop confirmations hold under adversarial multi-step prompts.
Memory Poisoning & Cross-Session Attacks
Long-running agents accumulate memory. Attacker-controlled content in that memory becomes a persistent compromise. We test memory writes for sanitisation, cross-user memory leakage in multi-tenant deployments, and the ability to detect and purge poisoned entries.
Multi-Agent Trust Boundaries
Agent-to-agent systems — supervisor + specialists, A2A, agent swarms over MCP — introduce new trust boundary problems. We test whether a compromised sub-agent can hijack its supervisor, whether tool call chains leak secrets across agents, and whether a tenant can leak into another tenant's agent context.
Audit Evidence & Compliance
Findings map to OWASP LLM Top 10, ISO 42001, NIST AI RMF, SOC 2 CC6.6 / CC7.1, GDPR Article 22 (where agents make automated decisions), and the EU AI Act high-risk-system obligations. One AI agent pen testing engagement produces compliance evidence for every applicable AI governance framework.
Frequently Asked Questions
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