Red-team AI without provider-side refusals.
Generative AI red teaming runs on adversarial probes most LLMs refuse to write. Our abliteration model writes them at scale: jailbreaks, prompt injection, RAG-exfiltration, agent tool-misuse, model-stealing probes, and the harmful-content tests responsible AI requires. Probe model and application. Cover malicious and benign personas. Run on every release.
waiting for prompt…
The red-teaming probes generated for your AI system.
Prompt-injection battery
Direct, indirect (PDF/HTML/URL), and ASCII-smuggling vectors. Application-specific payloads, not canned attack lists.
Jailbreak resilience scoring
DAN, roleplay, Pliny-style techniques. Regression-test against a maintained corpus on every release.
RAG document exfiltration
Generate queries designed to extract documents, system prompts, or PII from your knowledge base. Catch leaks before red-teamers do.
Agent tool-misuse
Coerce agents into unauthorized tool/function calls: file-system writes, SSRF, sandbox escapes, secret-environment reads, network-egress bypass.
Harmful-content probes
Probe for responsible-AI failures: harassment, violence, self-harm, illicit-activity glorification. Not just security. RAI dimensions too.
Adversarial training data
Generate red-team examples for fine-tuning in-house safety classifiers. Same governed API, JSONL output, full provenance.
Red-team the base model and the application.
You can't probe a base model that refuses your probes. Most production APIs respond to an adversarial test prompt with a refusal, which tells you the safety filter caught it but not what the foundation would actually produce unguardrailed. Our hosted model accepts the probes and returns the raw behavior, so the base layer finally shows up under test. Then point the same model at the application surface: your endpoints, RAG corpus, tool schemas, system prompts. Same model, two surfaces, full coverage.
Malicious and benign users break AI in different ways.
Most production APIs refuse to write the adversary side of your red-team corpus. Our model writes both sides: adversary prompts (DAN-style jailbreaks, tool-misuse coercion, indirect injection) and benign-user prompts (roleplay drift, pasted email with a hidden injection). Schema-locked with severity tags, ready to drop into your eval harness or detector training pipeline.
- DAN-style jailbreak
- Indirect injection via PDF metadata
- Prompt-leak via canary tokens
- Coerced tool-call to /etc/passwd
- Roleplay scenario goes off-rails
- Multi-turn drift into harmful content
- Unintended prompt-injection from a customer's pasted email
- Hallucinated dangerous instruction
AI red teaming isn't a one-shot test. It's a corpus on every release.
Frontier APIs get re-tuned every few weeks; your red-team corpus drifts with them, and a higher attack-success rate next release could be real or just test drift. Our hosted model is pinned and won't refuse, so the corpus stays the corpus. Regenerate on deterministic seeds, replay across releases, and your attack-success and refusal-bypass scores compare apples to apples. At API-call cost, not six-figure engagements.
Free tier. Pay-as-you-go. Enterprise.
Standard API rates. Per-engagement key scoping. Decision-log retention on Team and Enterprise.
Try the model that doesn’t say no.
Free tier. OpenAI-compatible. Policy Gateway when you scale.