AI Agent Security

Evaluate the security of your AI and LLM integrations, autonomous agents, and machine learning pipelines. We test for prompt injection, jailbreaking, data exfiltration through model responses, tool-use manipulation, training data poisoning vectors, and guardrail bypasses. As organisations deploy AI agents with access to production systems, these risks move from theoretical to critical.

Our Approach to AI Agent Security

We test your AI systems the way adversaries will — crafting prompt injections that bypass system instructions, manipulating agent tool-use to access unauthorised resources, extracting training data and system prompts through carefully structured queries, and testing guardrails against adversarial inputs designed to produce harmful or policy-violating outputs. Our methodology follows the OWASP LLM Top 10 and MITRE ATLAS framework, adapted for your specific AI architecture and deployment model.

Why This Matters

Identify prompt injection vectors that could manipulate AI agent behaviour
Test guardrail and safety control effectiveness against adversarial inputs
Assess data exfiltration risks through AI-generated responses and tool calls
Evaluate agent tool-use permissions to prevent unauthorised system access
Identify training data exposure risks and model inversion vulnerabilities
Receive actionable hardening recommendations specific to your AI architecture

What You Receive

Prompt injection and jailbreak test results with reproduction steps
Guardrail bypass analysis with technique documentation
Data exfiltration risk assessment through AI response channels
Tool-use permission and scope audit for autonomous agents
Model output manipulation findings with impact assessment
Training data exposure and privacy risk analysis
AI-specific threat model aligned to OWASP LLM Top 10
Hardening recommendations with implementation priority

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