Adversarial testing for AI-powered applications. Find what guardrails miss before attackers do.
As organizations deploy LLM-powered chatbots, AI assistants, and multimodal applications, the attack surface extends beyond traditional vulnerabilities. We conduct structured adversarial testing specifically designed for AI systems, testing the prompt layer, guardrail mechanisms, and safety filters that protect your users and data.
Our methodology is grounded in the OWASP LLM Top 10 and MITRE ATLAS frameworks, combined with hands-on adversarial prompt engineering techniques developed through testing production AI systems across multiple industries.
Direct and indirect prompt injection, instruction override, system prompt extraction, and context manipulation attacks against LLM-powered interfaces.
Systematic jailbreak testing using role-playing, encoding bypasses, multi-turn manipulation, and adversarial prompt mutation strategies.
Assessment of safety filters, content moderation systems, system prompt structures, and output validation mechanisms for bypass vulnerabilities.
Testing for training data extraction, PII exposure through model outputs, RAG context leakage, and sensitive information disclosure.
Adversarial inputs across text, image generation, and vision model interfaces. Testing image-based prompt injection and cross-modal attacks.
Security assessment of AI agents with tool access: testing for unauthorized actions, tool abuse, privilege escalation through agent chains.
Schedule a consultation to discuss adversarial testing for your LLM-powered systems.
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