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AI-native security testing.
Find it before attackers do.

Three automated capabilities — vulnerability discovery, penetration testing, and code audit. Finds what generic scanners miss in AI gateways, agents, and LLM applications.

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What we do

Three ways to find
what standard tools miss.

Each capability targets a different attack surface — run independently or combined. All findings feed the same cryptographic evidence chain.

Automated Vulnerability Discovery

Corpus-driven fuzzing and probing across API surfaces, configuration boundaries, and trust edges. Finds privilege isolation failures, key exposure paths, and multi-tenant boundary violations that generic scanners don't model. Every finding generates an adversarial regression fixture automatically.

Continuous · adversarial fixtures · regression CI

Penetration Testing Agent

An autonomous agent that explores your system's attack surface end-to-end — crafting adversarial inputs, chaining tool calls, and probing authorization boundaries the way a real attacker would. Delivers a signed findings report with reproduction steps and fixture tests.

Autonomous · end-to-end attack chains · signed report

Code Audit Agent

A fine-tuned code-audit LLM that reviews implementation-level flaws: unsafe deserialization, unvalidated inputs, secret leakage into logs, and authorization logic errors — trained on AI system codebases and real-world gateway vulnerability patterns.

LoRA · Qwen2.5-Coder 7B–32B · NVD CVE + OWASP GenAI

Audit case study

Applied to LLM Gateways:
600+ findings across 6 projects.

LLM gateways are one of our proven audit targets. Multi-tenant key management, routing logic, and plugin surfaces create vulnerability classes that only AI-aware tooling reliably catches.

Gateway-specific vulnerability classes

  • 1

    Privilege isolation failures — tenant A accessing tenant B's model keys or usage data.

  • 2

    Key exposure paths — provider API keys reachable via crafted requests or error responses.

  • 3

    Tool-call authorization bypass — skipping permission checks via prompt crafting or parameter manipulation.

  • 4

    Secrets in model context — credentials injected into retrieved context and returned in completions.

Deep dive: OSS gateway risk →
tracestone.io
Red-team evaluation — OWASP LLM Top 10 results

From findings to proof

Scan reports you can
actually hand to an auditor.

Findings become verifiable evidence.

Every finding is converted into a signed entry in the shared audit.jsonl protocol. Your security team gets a proof_hash that any auditor can reproduce locally with the open-source verifier — turning a "scan report" into an independently verifiable record.

Ready to audit your AI stack?

Point our audit agent at your environment — gateway, agent, or codebase. We deliver a findings report with cryptographic evidence your team can verify offline.

Request an audit