Evo Agent Red Teaming - Experimental Preview
AI systems behave unpredictably. Evo Agent Red Teaming adversarially tests AI applications — so you can ship with confidence that your AI meets market security standards.
AI apps rely on prompts, context, and orchestration logic.
Traditional scanners don’t test the components that determine your AI system’s behavior.
AI agents can take actions across systems.
A successful prompt injection attack can trigger data exfiltration, privilege escalation, or unintended system actions.
The same prompt can produce different outcomes depending on context.
Static scanning alone can’t prove an AI system is secure.
Evo Agent Red Teaming simulates adversarial prompts and evaluates how your system responds, helping teams identify exploitable behaviors before attackers do.
Launch targeted adversarial prompts to your AI endpoints to simulate real attack techniques such as prompt injection, sensitive data exposure, and unsafe outputs.
Test how prompts, tools, and data sources interact during an attack.
Classify each finding against leading industry security frameworks: OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF.
Use scan results to demonstrate compliance posture and generate defensible evidence for auditors and stakeholders without requiring any post-processing.
Each finding includes reproducible attack payloads and system responses, helping teams validate vulnerabilities and prioritize fixes based on real exploitability.
Red teaming tests can run from the CLI or within CI/CD pipelines, allowing teams to continuously validate AI security as prompts, models, and agent workflows evolve.
Validate that AI systems meet emerging security expectations.
Continuous red teaming provides defensible evidence of how applications behave under attack.
Prevent agent-driven incidents before they happen.
Continuously test AI endpoints for vulnerabilities like prompt injection and sensitive data exposure.
Innovate without breaking trust.
Evaluate how prompts, models, and tools behave under adversarial conditions. Use exploit evidence to understand risk and improve AI system design.
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