RedEval is a red-teaming platform for LLMs and AI agents — generate adversarial batteries, run multi-turn attacks, and judge every response across safety, relevance, and compliance.
From single-shot jailbreaks to multi-turn agentic compromise — RedEval covers the full surface area of LLM risk.
Generate targeted jailbreak and injection prompts tailored to your system's role and data access.
Escalate across a conversation, priming the model turn by turn until its defenses slip.
Score every response for relevance and safety with GPT-4o and Claude, side by side.
Plant instructions inside documents, tool outputs, and retrieved context to test what your agent ingests.
Probe function-calling agents for unsafe actions, scope creep, and confused-deputy failures.
Surface account data, secrets, and system-prompt exposure before attackers do.
Stress-test outputs across demographic and adversarial framings for harmful or skewed responses.
Measure factual drift and unsupported claims under pressure and ambiguity.
Map findings to OWASP LLM Top 10, NIST AI RMF, MITRE ATLAS, and the EU AI Act.
Re-run suites after every model or prompt change to catch reopened vulnerabilities.
Single prompts are easy to refuse. Crescendo attacks prime the model turn by turn — each message benign on its own — until the guardrails slip. RedEval scripts the escalation and scores where the defense broke.
Define the target system and the data, tools, and permissions it has access to.
Produce attack batteries and multi-turn scenarios across the risk categories you select.
Dual-model judging scores every response for safety and relevance.
Findings are mapped to severity and to the frameworks your governance team already uses.
Beyond the platform, our specialists design custom attack templates, run hands-on adversarial assessments, and map results to your governance requirements.