AI Security Testing
AI is shipping fast. Security testing isn't keeping up. We manually test your AI integrations, LLM-powered features, and vibe-coded applications to find the bugs that automated scanners miss entirely.
What We Test
Manual security testing for the new generation of AI-powered applications
LLM Integrations
ChatGPT, Claude, Gemini APIs integrated into your applications. We test for prompt injection, data leakage, and model manipulation.
AI Agents & Copilots
Autonomous agents with tool access, code execution, or system privileges. We test for privilege escalation and unintended actions.
Vibe-Coded Apps
Applications built with AI assistance (Cursor, Copilot, etc.) often have subtle security bugs. We find them before attackers do.
RAG Systems
Retrieval-augmented generation pipelines with access to your data. We test for data poisoning, context manipulation, and exfiltration.
AI-Powered Security Tools
Using AI for threat detection or response? We test whether attackers can evade or manipulate your AI defenses.
Custom ML Models
Proprietary models deployed in production. We test for adversarial inputs, model extraction, and membership inference.
Bugs We Find
AI introduces new attack surfaces that traditional testing doesn't cover
Prompt Injection
User input that hijacks LLM behavior, bypasses safety filters, or executes unintended commands. Direct and indirect variants.
Data Leakage
AI systems that expose training data, system prompts, internal configurations, or other users' information through clever queries.
Insecure Tool Use
AI agents with access to tools (code execution, APIs, databases) that can be manipulated into taking harmful actions.
Jailbreaks & Bypasses
Techniques to circumvent safety guardrails, content filters, or role restrictions. We test resilience against known and novel attacks.
The Vibe Coding Problem
AI-assisted development is fast but introduces security debt that's invisible to traditional tools
What AI Code Gets Wrong
- Missing input validation that looks correct at first glance
- Authentication logic with subtle bypass conditions
- SQL/NoSQL queries that are almost parameterized
- Outdated crypto patterns from training data
Why Scanners Miss It
- Code follows patterns but misses edge cases
- Business logic flaws require context to find
- Chained vulnerabilities across AI-generated components
- Novel bugs not in scanner signature databases
Our Approach: We combine traditional application security testing with AI-specific attack techniques. Manual testing by humans who understand both security and how AI systems actually fail.
Shipping AI? Let's Talk.
Best for: Teams deploying LLM-powered features, AI agents, or applications built with AI coding assistants who need manual security validation beyond automated scanning.
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