What we look for
AI and LLM vulnerabilities we hunt for
AI systems introduce an attack surface traditional testing misses. We probe LLM apps and ML pipelines for prompt injection, data leakage, and unsafe tool use.
Prompt Injection
Manipulating model behavior through crafted direct or indirect input.
We test for
- Direct and indirect prompt injection
- System-prompt extraction
- Instruction and guardrail bypass
- Jailbreak techniques
Sensitive Data & Model Leakage
Getting the model to reveal training data, secrets, or other users context.
We test for
- Training-data and PII leakage
- System-prompt and secret disclosure
- Context and memory bleed
- Output filtering gaps
Insecure Output & Tool Use
Unsafe actions taken downstream on the basis of model output.
We test for
- Excessive agency and tool abuse
- Unsafe downstream execution
- Output handling flaws
- Privilege and action limits
RAG & Pipeline Abuse
Attacking the data, retrieval, and embedding layer behind the model.
We test for
- Retrieval poisoning
- Data-source injection
- Embedding and index abuse
- Access control on sources
How it works
How your engagement runs
From scope through the final retest, your team stays in the loop at every step,
with findings tracked live in our platform.
- 01
Scope & kickoff
Targets, roles, and rules of engagement defined in writing, with a fixed scope and timeline.
- 02
Testing goes live
Findings post to your live platform dashboard the moment our testers confirm them.
- 03
Track remediation
Follow every finding from open to fixed, with severity, evidence, and status in one place.
- 04
Report & retest
Executive and technical reports land, then request a free retest in one click.
Resources
Field notes from the offensive side
Offense in Depth in Red Team Operations
Defense in depth layers protection. Offense in depth layers attack paths so a red team still reaches its objective when one route fails. Here is how it works.
Security Between Penetration Tests
An annual pentest covers two weeks and leaves fifty uncovered. Here is how to secure the rest of the year without waiting for the next scheduled engagement.
The Limits of AI in Penetration Testing
AI is changing penetration testing, but it will not replace human testers. Here is what it does well, where it falls short, and why judgment still wins.
Want to see a real report first?
Request a redacted sample report before you scope an engagement.
FAQ
Frequently asked questions
What teams most often ask before
scoping AI / ML penetration testing.
01What does AI/ML penetration testing cover?
We assess LLM-powered applications and ML pipelines for prompt injection, jailbreaks, sensitive-data and model leakage, unsafe tool use, and retrieval (RAG) abuse, aligned to the OWASP Top 10 for LLM Applications.
02Do you test our guardrails and system prompts?
Yes. We test how well your guardrails, filters, and system prompts hold up against real bypass and extraction techniques, then give you hardening guidance for the prompts, tooling, and pipeline.
03How long does an AI/ML penetration test take?
Timelines depend on the number of models, integrations, and tools involved, but most engagements run about one to two weeks, followed by reporting and a complimentary retest.
Ready to test your defenses?
Talk to our team about scoping AI / ML penetration testing.
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