Pentesting AI & LLM Penetration Testing
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AI & LLM Penetration Testing

LLM-powered features fail in ways traditional application testing never checks for. Prompt injection, data leakage through the model, poisoned retrieval, and agents that can be talked into using their tools against you are live risks the moment you ship an AI feature, and a standard web-app pentest will walk straight past all of them.

RedSecLabs tests AI and LLM applications against the OWASP Top 10 for LLM Applications (2025) and the NIST AI Risk Management Framework, combining adversarial prompt engineering with the deep application and infrastructure testing the surrounding system still needs. Our testing is human-led by CREST-accredited security engineers, not a guardrail scanner, because the interesting attacks on AI systems are the ones a checklist cannot anticipate. Our AI and LLM testing methodology is led by Rafay Baloch, RedSecLabs’ founder and a security researcher with a track record of browser and web-application vulnerability research.

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✓ Maps to OWASP LLM Top 10 (2025) · ✓ NIST AI RMF aligned · ✓ Human-led adversarial testing · ✓ Agentic & RAG coverage · ✓ CREST-accredited team
Who this is for

This service fits if you’re..

1
Shipping AI features
Product teams adding LLM chat, copilots or generation features to an existing application.
2
Building AI agents
Teams deploying agents with tool access, function calling or autonomous workflows that act on real systems.
3
Enterprise & regulated buyers
Companies whose customers now ask specifically how their AI features have been security tested.

AI & LLM Penetration Testing, Quick Facts

Last reviewed: 2026-07-21
Primary framework
OWASP Top 10 for LLM Applications (2025), with prompt injection ranked LLM01 for the second edition running
Also aligned to
NIST AI Risk Management Framework (AI RMF) and its Generative AI profile
What we test
The model interface, the application around it, the RAG/retrieval pipeline, and any agent tool integrations
Signature risks
Prompt injection (direct and indirect), sensitive data disclosure, insecure output handling, excessive agency, RAG/vector poisoning
Approach
Human-led adversarial testing plus conventional app/infra testing, not a guardrail scanner alone
Deliverable
A report mapped to the OWASP LLM categories with proof-of-concept exploits and remediation, plus a free retest
LLM01
Prompt injection, the #1 OWASP LLM risk
10
OWASP LLM risk categories tested
2025
OWASP LLM Top 10 edition
CREST
Accredited testers

What AI penetration testing actually covers

An LLM feature is not one attack surface, it is several stacked on top of each other. There is the model interface itself, where prompt injection lets an attacker override instructions or extract the system prompt. There is the application around it, where insecure output handling means model output flows into a browser, database or shell and executes. There is the retrieval layer, where a RAG pipeline can be poisoned so malicious content is pulled into trusted context. And with agents, there is the tool layer, where excessive agency means the model can be manipulated into taking real actions, sending data, calling APIs, running code, on the attacker's behalf.

OWASP formalised these in the Top 10 for LLM Applications, and prompt injection has held the top spot (LLM01) across both editions because models process instructions and data in the same channel with no clean separation. Neither RAG nor fine-tuning fully removes it, which is why regular adversarial testing, not a one-off guardrail, is the control that matters. We test the whole stack the way an attacker approaches it, then hand back proof-of-concept exploits rather than a list of theoretical risks.

What our AI/LLM penetration test examines:
Direct and indirect prompt injection, including system-prompt extraction
Sensitive information disclosure through the model, logs and embeddings
Insecure output handling where model output reaches browsers, databases or shells
Excessive agency: agents manipulated into misusing their tools and permissions
RAG and vector-store attacks, including retrieval poisoning and tenant leakage
The conventional app and infrastructure security the AI system still depends on

The output is a report structured against the OWASP LLM Top 10, with working proof-of-concept exploits and remediation your engineers can act on, plus a retest to confirm fixes hold.

Why a normal pentest misses AI risk

Traditional application security assumes deterministic behaviour: the same input produces the same output, and you can enumerate the paths through the code. LLMs break that assumption. Their behaviour is stochastic, they follow instructions hidden in data they were only meant to read, and they can be induced to act well outside their intended function. A scanner running known-vulnerability checks has nothing to match against, and a web-app tester who has not built an adversarial-prompt methodology will not find the injection that turns your helpful assistant into an exfiltration tool.

Guardrail products help, but they are a control to be tested, not a substitute for testing. Vendors and researchers have repeatedly shown that guardrails alone are not enough, prompt injection has been called the new SQL injection for exactly this reason. The gap is real, and it is where AI-specific testing earns its place.

What generic testing leaves exposed:
Prompt injection, invisible to signature-based scanners entirely
Indirect injection via documents, emails or web content the model retrieves
Agent tool-abuse chains that only appear under adversarial prompting
Data leakage through the model that no network test would surface
RAG poisoning, because the knowledge base is wrongly assumed trusted
Over-reliance on guardrails that were never actually stress-tested

AI security is not traditional app security with extra steps; it is a distinct discipline. Testing it as though it were the former is how the headline AI incidents keep happening.

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OWASP LLM Top 10 coverage

The 2025 OWASP Top 10 for LLM Applications risks, and how our testing addresses each. Prompt injection has ranked #1 in both editions of the list.

OWASP refRiskHow we test it
LLM01Prompt injectionDirect and indirect injection, jailbreaks, system-prompt extraction against your live prompts
LLM02Sensitive information disclosureProbing for leakage via responses, logs, embeddings and cached context
LLM05Improper output handlingTesting whether model output executes as code, SQL or script downstream
LLM06Excessive agencyManipulating agents into misusing tools, functions and permissions
LLM08Vector & embedding weaknessesRAG retrieval poisoning and cross-tenant data exposure in vector stores
+4 moreSupply chain, data poisoning, system-prompt leakage, unbounded consumptionAssessed where relevant to your architecture and scope

Based on the OWASP Top 10 for LLM Applications (2025). Scope is tailored to your architecture; not every category applies to every system.

Test Your AI Before an Attacker Does

Tell us what you’ve built, a chatbot, a copilot, an agent, and we’ll scope an OWASP-aligned AI penetration test with a fixed quote within one working day.

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Where to next

Related services worth exploring.

Web App Pentesting

The application-layer testing your AI feature still needs

API Pentesting

Test the APIs your model and agents call

Threat Modelling

Design-stage risk analysis for AI features

Frequently Asked Questions

It is security testing focused on the risks unique to applications built on large language models, prompt injection, data leakage through the model, insecure handling of model output, agent tool abuse, and attacks on retrieval pipelines. It complements, rather than replaces, conventional application and infrastructure testing of the surrounding system. We align it to the OWASP Top 10 for LLM Applications (2025) and the NIST AI RMF.

A normal pentest assumes deterministic behaviour and enumerable code paths. LLMs are stochastic, act on instructions hidden in data, and can be induced to misuse tools, none of which a signature-based scanner or a standard app tester will catch without an adversarial-prompt methodology. We do both: AI-specific adversarial testing plus the conventional testing the system still requires.

Prompt injection is when an attacker slips instructions into user input or into content the model reads, causing it to ignore its intended rules. It is OWASP's number one LLM risk (LLM01) in both editions of the list. Because models process instructions and data in the same channel, there is no known fool-proof prevention, only defence-in-depth and regular adversarial testing, which is exactly why this testing exists.

Yes, and this is increasingly the highest-risk area. Where an LLM has tool access, function calling or autonomous workflows, we test for excessive agency: whether the agent can be manipulated into sending data, calling APIs, or executing actions it should not. Agentic AI risk is a core focus of the 2025 OWASP list and our methodology.

No. Guardrail products are a useful control, but they are something to test, not a replacement for testing. Researchers have repeatedly bypassed guardrails, and prompt injection has been described as the new SQL injection precisely because point defences keep falling. We test whether your guardrails actually hold under adversarial pressure.

Primarily the OWASP Top 10 for LLM Applications (2025) and the NIST AI Risk Management Framework, including its Generative AI profile. Where you have sector obligations, ISO 42001, EU AI Act readiness, or SOC 2 covering AI features, we structure findings so they support those programmes too.
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