Auditing the AI you ship

Agents and LLM features

An agent that reads untrusted input and then acts on it is a confused deputy waiting to happen. We test for prompt injection that actually does something, not just makes the model misbehave.

Prompt injection with real impact: tool abuse, data exfiltration, code execution
Agent sandbox and tool-permission escapes
Excessive agency: what the agent can reach that it should not
Data exposure across users, tenants and system prompts
Supply-chain risk in open-weight and fine-tuned models

Auditing the AI in your pipeline

AI used to build and secure your product

Teams increasingly let models write code and gate releases. That helps, until it quietly ships an insecure default. We review where AI touches your development and security process and where it can be trusted to.

Security review of AI-assisted and AI-generated code
Guardrails and evaluation for models embedded in the SDLC
Risk of backdoored or tampered model weights

What you get

Findings you can act on

Each finding reproducible, with the exact prompt or payload and a proof-of-concept
A management summary and a technical report with a clear order to fix in
Concrete mitigations for the attack paths we proved
A walkthrough with a Q&A round, and a retest once you have remediated

AI Security Audit

Agents and LLM features

AI used to build and secure your product

Findings you can act on

Have us test your AI