Free AI safety stack.
Frontier adversarial red teaming.

350+ verified vulnerabilities across 195+ AI/ML repositories. 166 responsible disclosures. Two novel vulnerability classes discovered.

MIT LicensedSelf-hostableZero vendor lock-in
TRACK RECORD

We break AI safety systems. Then we build better ones.

Offensive security methodology applied to AI safety evaluation. Find the gaps. Document them. Ship the fix.

195+ repos

Systematic Audit of AI/ML Infrastructure

Scanned 195+ repositories across NVIDIA, Microsoft, Meta, Google, HuggingFace, OpenAI, and 50+ other organizations. 350+ verified vulnerabilities. 166 formal disclosure reports.

2 novel classes

Two Novel Vulnerability Classes Discovered

Identified two previously undocumented vulnerability classes affecting model serialization formats and sandboxed code execution environments. Details pending coordinated disclosure.

166 disclosures

Responsible Disclosures Across the ML Ecosystem

Critical and high-severity findings in PyTorch, DeepSpeed, BentoML, TorchServe, Ray, Ollama, vLLM, LangChain, and dozens of production ML systems. Coordinated disclosure in progress.

PR #798

ControlArena Contribution

Accepted pull request to UK AISI's ControlArena benchmark. Demonstrated monitor prompt injection -- agents evading their own safety oversight.

RED TEAMING

Adversarial red teaming for AI systems.

Same pipeline that found 350+ vulnerabilities across NVIDIA, Microsoft, Meta, Google, and HuggingFace infrastructure. Applied to your systems. Scoped engagements with CVE-quality findings.

ML Infrastructure Audit

Automated adversarial analysis of your ML stack. Deserialization, injection, auth bypass, model format exploits, supply chain. Same methodology behind 350+ verified vulnerabilities across production systems at NVIDIA, Microsoft, Meta, and Google.

AI Safety Evaluation Testing

We test the evaluators. Monitor bypass, compound judge failures, signal dilution, sandbox escapes. Your safety infrastructure is an attack surface -- we prove it before an adversarial agent does.

Agent Red Teaming

Systematic adversarial campaigns against your agents and tool integrations. Privilege escalation, exfiltration, goal hijacking, memory poisoning. CVE-quality findings with reproduction steps.

Engagement model

We scope. We test. You get a report. No retainers, no ongoing fees unless you want them. Typical engagement: 2-4 weeks.

Book a Scoping Call
FREE SAFETY STACK

The tooling we built to do our job. Now yours.

Six packages. All free. All MIT licensed. Download the full stack or pick individual tools.

These started as internal tools for our adversarial research. Policy engine to test guardrails. Content scanner to probe classifiers. Monitor to detect behavioral drift. We use them daily. You should too.

$npx @authensor/create-authensor my-agentCopy
INTEGRATION

One line. Full safety.

Wrap any agent action with guard() and policy evaluation, content scanning, and audit logging happen automatically.

terminal
# Download the full safety stack
npx @authensor/create-authensor my-agent
cd my-agent && npm install

# Or install individual tools:
npm install @authensor/aegis        # Content scanner
npm install @authensor/sentinel     # Behavioral monitor
npm install @authensor/engine       # Policy engine
npm install @authensor/mcp-server   # MCP Gateway
npm install @authensor/redteam      # Red team harness
WORKS WITH
LangChainOpenAICrewAIClaudeVercel AI SDKMCP
WHO WE ARE

Adversarial AI safety researchers.

We apply offensive security methodology to AI safety evaluation. Penetration testing for guardrails. Red teaming for agents. Adversarial probing for classifiers.

The safety stack is our toolkit, open-sourced. The red teaming is what we do with it.

195+ repos audited. 350+ verified vulnerabilities. 166 responsible disclosures. 2 novel vulnerability classes discovered. ControlArena contributor (UK AISI).

Two ways to work with us.

Download the free safety stack. Or hire the team that built it to red team your systems.