AI Agent Security Audit

No. Adversarial testing runs against an isolated copy of your agent in a sandboxed environment using synthetic accounts and synthetic test data. Your production agent is never invoked, never receives test prompts, and never sees test customer data. The static review works from your system prompts, tool descriptions, MCP server definitions, and configuration alone.

All major model providers - Anthropic Claude, OpenAI GPT, Google Gemini, Meta Llama, Mistral, Cohere, AWS Bedrock, Azure OpenAI, Vertex AI, Vercel AI Gateway, and self-hosted (vLLM, Ollama, Together, Groq). All major frameworks - LangChain, LangGraph, LlamaIndex, CrewAI, Microsoft AutoGen, Semantic Kernel, Vercel AI SDK, Pydantic AI, Anthropic Agent SDK, OpenAI Agents SDK, Mastra, Inngest Agent. And all major IDE / agent runtimes - Claude Code, Cursor, Windsurf, Cline, Replit Agent, and Devin. Including custom in-house frameworks.

MCP servers are the new attack surface most teams have not accounted for. Tool description poisoning, confused-deputy attacks across MCP servers, indirect prompt injection through MCP-retrieved content, and over-permissive MCP grants (filesystem, GitHub, databases, browsers) are now among the highest-impact agent vulnerabilities. We audit every MCP server in your stack against the OWASP Agentic AI Threats T1 Excessive Agency, T2 Memory Poisoning, T7 Tool Misuse, and confused-deputy classes - and produce a per-MCP scope-reduction backlog.

Both. We run adversarial test suites (HarmBench, AdvBench, JailbreakBench, GCG, PAIR, Crescendo, custom indirect-prompt-injection corpora) against the isolated copy of your agent, then a senior AI-security engineer manually constructs multi-step attack chains specific to your architecture - for example, indirect prompt injection via a customer-uploaded PDF that gets retrieved by RAG and triggers a tool call to send email with exfiltrated context. Static config review alone misses these chains; adversarial testing alone produces noise without context.

Yes. Every finding is mapped to specific articles of the EU AI Act (high-risk system requirements - Article 9 risk management, Article 10 data governance, Article 12 record-keeping, Article 14 human oversight, Article 15 accuracy and cybersecurity), ISO/IEC 42001 AI management system controls, NIST AI Risk Management Framework + Generative AI Profile (Govern / Map / Measure / Manage), and MITRE ATLAS adversarial-ML tactics. The report drops directly into your AI compliance evidence pack.

It depends on your stack and threat model. We assess your current coverage and recommend from NVIDIA NeMo Guardrails, Guardrails AI, Lakera Guard, Protect AI, AWS Bedrock Guardrails, Azure AI Content Safety + Prompt Shields, Google Vertex AI Safety Filters, and OpenAI Moderation API for guardrails - plus Langfuse, LangSmith, Helicone, Arize Phoenix, WhyLabs, Honeycomb, and Datadog LLM Observability for AI observability. The report includes a concrete deployment plan, not just a tool list.

Yes. RAG poisoning, vector-store data leakage (Pinecone, Weaviate, Qdrant, pgvector, Chroma, Azure AI Search, Vertex AI Vector Search), embedding inversion attacks, sensitive-document classification in indexes, retrieval-time access control (per-user / per-tenant filtering), and grounding / citation enforcement are all in scope - because RAG is the most common indirect-prompt-injection vector in production agents today.

Typical turnaround is 5-7 business days from configuration delivery and isolated-copy access, plus a 45-minute live findings walkthrough at a time that suits your AI engineering, security, and product leads. Larger agent estates with many MCP servers, multiple frameworks, or high-risk EU AI Act scope can take a little longer; we confirm the timeline as soon as we see the scope.