AI Penetration Testing
Prompt Injection Testing
An LLM app glues hidden rules, retrieved documents, and the user's message into one block, and the model cannot tell instructions from data. We test direct injection through the chat and indirect injection, where the payload hides in data the model reads later, and we show what an attacker gains from each.
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System Prompt & Data Extraction
The system prompt holds the exact guardrail wording an attacker needs and often holds secrets it should never hold. We extract it with verbatim dumps, reframes, and encoded output, then read it for API keys, internal URLs, and the rules we will break next.
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Jailbreaks & Guardrail Bypass
Refusals are a learned habit, not a hard block. Where safety testing is in scope, we work through multi-turn Crescendo, Skeleton Key, Policy Puppetry, and encoding tricks like invisible Unicode and TokenBreak, and we map which layer of your guardrail stack each one beats.
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RAG & Vector Store Attacks
Whatever your model retrieves, it treats as fact. If we can write to a retrieved source, we poison it with authoritative fake policy and watch the model obey. We also test cross-tenant retrieval, secrets indexed by mistake, and embedding inversion.
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Tools & Agentic Attacks
If the model can act, it is the best target on the page. We fuzz every tool argument for SQLi, command injection, and SSRF, test unauthorized calls and confused-deputy chains, and escalate code interpreters step by step to real code execution.
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Data Exfiltration
Injection is only half the chain; stolen data needs an exit. We build zero-click channels out of your app: markdown images that carry secrets into our server logs, link previews that leak on unfurl, and tool egress through fetch, email, and webhook functions.
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Reasoning-Model Attacks
On thinking models, the chain of thought is one more text stream in the context, so it can be padded, steered, faked, or cut short. We test reasoning dilution, thinking-budget steering, forged reasoning traces, and structured-output coercion.
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Agentic Frontier & Supply Chain
Agents that trust other agents, load skills, and pull model weights open a new surface. We test AI-to-AI injection, tool rug pulls and squatting, sleeper payloads, prompt worms, rules-file backdoors, and code execution through untrusted weights.
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Every Deployment
The deployment decides what is in scope and what extra surface exists. We hunt leaked API keys on third-party APIs, exposed inference servers and pickle-based supply chain RCE on self-hosted stacks, cloud metadata SSRF and IAM issues on Azure OpenAI, Bedrock, and Vertex, and memorized training data in fine-tunes.
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Method & Reporting
We map the attack surface before we attack anything, automate coverage with garak, PyRIT, and promptfoo, and prove every finding with the exact working prompts, video evidence, and mapping to the OWASP LLM Top 10 and MITRE ATLAS.
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