AI & LLM Security Pentesting
Defensive validation of Artificial Intelligence models, Large Language Models (LLMs), prompt injection vectors, and data leakage channels.
Last reviewed by Rhitik Kumar, Lead Offensive Security Engineer.
Scope of Assessment
We inspect AI pipelines. We test for prompt injections, model training data extraction, insecure output handling, denial of service (DoS), and data poisoning vectors.
Engagement Details
How the engagement runs
- 01
Scoping and architecture review
We map the pipeline: models in use, system prompts, retrieval sources, tool and function calls, agent autonomy, and where untrusted input enters.
- 02
Prompt and input attacks
Direct and indirect prompt injection is tested against the OWASP Top 10 for LLM Applications, including payloads planted in documents and web content the model later retrieves.
- 03
Tool, data and output testing
Where the model calls tools or agents act autonomously, we test whether an injected instruction can trigger real actions, leak retrieval data across tenants, or return output that downstream code executes unsafely.
- 04
Reporting and retest
Each finding ships with the exact prompt or payload that triggered it, plus guardrail and architecture fixes, and one retest within 30 days.
What you receive
- Findings mapped to the OWASP Top 10 for LLM Applications
- Reproducible payloads for every successful injection
- Tenant isolation and retrieval leakage results
- Guardrail, system prompt and architecture recommendations
- Retest report after remediation
Frequently Asked Questions
What is OWASP Top 10 for LLMs?
It is a standard list of model security flaws. We audit your agents against this list, covering prompt injection and training data leaks.
Do you need access to the model weights?
No. Most testing is black-box against your deployed application, which is the surface an attacker actually reaches. White-box access to prompts and pipeline code makes the test faster and deeper.
Do you test agents that take real actions?
Yes, and that is usually where the serious findings are. Tool-calling agents are tested in a sandbox first, then against agreed non-destructive actions.
Is this different from testing the app around the model?
It is additive. The surrounding web and API surface is tested conventionally; the model layer needs injection, leakage and output-handling tests that traditional pentesting does not cover.
Related security services
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