Secure AI · AI & LLM Security Testing
Red-team your AI before someone else does
LLM features and autonomous agents introduce attacks that traditional pentests don't cover. We test chatbots, copilots, RAG systems and tool-using agents for prompt injection, sensitive-data leakage, unsafe tool use and guardrail bypass, and show you how to fix them without killing the product experience.
Outcomes
What you walk away with
Direct and indirect prompt injection paths identified
Leakage of system prompts, PII and other tenants' data prevented
Agents constrained to least-privilege tool access
Guardrails tested against real jailbreak techniques
Coverage
What's in scope
What we test
- Direct and indirect prompt injection
- Sensitive information and system-prompt disclosure
- Excessive agency and tool/function-call abuse
- RAG poisoning and vector-store access control
- Insecure output handling (XSS, SSRF, code exec via LLM output)
- Cost and resource abuse (unbounded consumption)
Systems
- Customer-facing chatbots and copilots
- RAG and enterprise knowledge search
- Autonomous and multi-agent workflows, MCP servers
- Model APIs and AI gateways
AI agents (OWASP Agentic Top 10)
- Agent goal hijack through content the agent reads
- Tool misuse and exploitation, including MCP servers and plugins
- Identity and privilege abuse of delegated credentials
- Unexpected code execution from generated code
- Memory and context poisoning
- Insecure communication and trust between agents
Around the model
- Classic bugs reached through the LLM: XSS, SSRF, IDOR and data exposure
- Cross-tenant retrieval and access control in RAG
- Approval gates, sandboxing and least privilege for agent actions
- Logging and monitoring of prompts, tool calls and refusals
Deliverables
- Findings mapped to the OWASP Top 10 for LLM Applications 2025, the OWASP Agentic Top 10 and MITRE ATLAS
- Reproducible attack prompts and transcripts
- Guardrail, architecture and permission recommendations
- Retest after fixes
Standards & frameworks
- OWASP Top 10 for LLM Applications 2025
- OWASP Top 10 for Agentic Applications
- CSA MAESTRO
- MITRE ATLAS
- NIST AI RMF
Ideal for
- Products shipping LLM-powered features to customers
- Teams deploying internal agents with access to company data
- Companies facing AI-specific questions in security reviews
FAQ
AI & LLM Security Testing questions
What is an LLM penetration test?
An LLM penetration test is a hands-on attack against an AI feature, chatbot or RAG pipeline. Testers try direct and indirect prompt injection, jailbreaks, system prompt and data leakage, insecure output handling, and abuse of connected tools and plugins. Findings are mapped to the OWASP Top 10 for LLM Applications 2025 and MITRE ATLAS, with fixes for prompts, guardrails and the surrounding code.
How do you security test an AI agent?
We attack the agent's goals, tools, identity, memory and connections to other agents. We try to hijack it through injected instructions, make it misuse tools or credentials, poison its memory, and push it into actions beyond its intended authority. Findings map to the OWASP Top 10 for Agentic Applications and the CSA MAESTRO layers.
Do you test MCP servers?
Yes. We test MCP servers and clients for authorization flaws, tool poisoning, excessive permissions and injection through tool descriptions and results.
How is this different from AI red teaming?
AI red teaming often focuses on harmful or biased model output. LLM penetration testing focuses on security: data leakage, unauthorized actions and compromise of the systems around the model. We cover both when you need them.
How long does an LLM pentest take?
A focused chatbot or RAG feature usually takes one to two weeks. Larger agentic systems take longer. We confirm timing after a free scoping call.
Which models and providers do you test?
Model-agnostic: OpenAI, Anthropic, Google, open-weight models and custom fine-tunes. We test your application around the model, which is where most exploitable risk lives.
Can't the model provider's safety filters handle this?
Provider filters address generic harmful content. They don't know your data, tools or permissions, so they can't stop an attacker from abusing your specific integration.
Related
Often combined with
AI Threat Modeling
Design-stage threat modeling for LLM apps and agentic systems: data, tools, autonomy and trust.
AI Application Security
Secure code review and guardrail engineering for the code that wires LLMs into your product.
Threat Modeling
Map how your system can be attacked before code ships, and decide which controls actually matter.
Ready to talk about ai & llm security testing?
Tell us what you're building. In a free 30-minute scoping call we'll map your risks and recommend the right engagement, whether you need a pentest, a threat model or an AI security review.