AI / LLM Security Testing
Your AI isn't just a feature — it's a new attack surface. We test it like one.
Why This Matters
Traditional security testing doesn't cover AI-specific vulnerabilities. Prompt injection is the new SQL injection — and most AI features ship with zero security testing.
LLM-powered features are being integrated everywhere — chatbots, code assistants, content generators, data analyzers. But they introduce entirely new attack surfaces: prompt injection, training data extraction, model manipulation, and agent hijacking.
We test your AI integrations with attack techniques from the latest research — indirect prompt injection through untrusted data, system prompt extraction, output manipulation, tool-use exploitation, and multi-turn conversation attacks that bypass safety guardrails.
Key Focus Areas
Prompt Injection
Direct and indirect prompt injection attacks — manipulating LLM behavior through user input, embedded documents, and external data sources.
System Prompt Extraction
Techniques to extract system prompts, internal instructions, and confidential context that was not meant to be exposed to users.
Training Data Leakage
Probing for memorized sensitive data from training sets, PII extraction, and data reconstruction attacks.
Agent & Tool Abuse
Testing AI agents with tool access — function calling manipulation, unauthorized action execution, and agent hijacking through conversation.
Output Manipulation
Bypassing content filters, generating harmful/biased content, and manipulating AI-generated outputs for social engineering.
Guardrail Bypass
Testing safety guardrails, content policies, and output validation — finding bypasses through encoding, role-playing, and multi-turn attacks.
How We Work
AI Feature Mapping
Understanding all LLM integration points, agent capabilities, tool access, and data sources that feed into the AI system.
Prompt Injection Testing
Systematic testing of direct and indirect injection vectors — user inputs, uploaded documents, and external data processed by the LLM.
Guardrail Assessment
Testing content filters, safety mechanisms, and output validation for bypass techniques using latest research.
Agent Security Testing
If AI has tool access — testing for unauthorized actions, privilege escalation through tool calls, and data exfiltration via agent.
Data Leakage Testing
Probing for system prompt exposure, training data extraction, and user context leakage across sessions.
Risk-Based Reporting
Findings categorized by real-world exploitability with specific mitigation strategies for each AI-specific vulnerability.
What You Get
- AI Security Assessment Report
- Prompt Injection PoC Library
- Guardrail Bypass Documentation
- Agent Security Audit Results
- AI-Specific Remediation Guide
- Ongoing monitoring recommendations
Tools & Frameworks
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