LLM Application Penetration Testing
AI-powered apps still have classic web bugs — plus new LLM-specific ones. We test the whole thing end to end: prompt injection, authorization, data exposure and the traditional flaws around the model.
What is LLM Application Penetration Testing?
AI-powered apps still have classic web bugs, plus new LLM-specific ones. We test the whole thing end to end — prompt injection, authorization, data exposure and the traditional flaws around the model — because attackers don't respect the boundary.
An LLM application is still an application: it has authentication, authorization, APIs and data stores, all of which can fail in the classic ways — plus a model that introduces prompt injection, data leakage and unsafe output on top. Attackers don't care which category a bug falls into; they chain them. We test the whole system end to end so you're not left with a hardened model bolted onto an insecure app.
LLM application testing also resists a tempting shortcut: hardening the model and declaring victory. Attackers don't respect that boundary — they'll chain a prompt injection with a classic authorization bug, or abuse an integration the model touches. Testing the whole system, model and app and infrastructure together, is the only way to find the chains that a model-only or app-only test would each miss.
LLM Application Penetration Testing is a manual, exploit-driven assessment: real people attacking your target the way an adversary would, then proving each finding with a working proof of concept. It is not an automated scan with a logo on it — the point is to find and demonstrate the issues that actually put you at risk, and hand your team a clear plan to fix them.
Why it matters
AI systems introduce an entirely new attack surface on top of the classic ones. Prompt injection, unsafe tool use and data leakage are already being exploited in production, and traditional testing doesn't cover them.
A llm application penetration testing engagement replaces guesswork with evidence. Instead of hoping your controls hold, you get a ranked, reproducible picture of where they don’t — before someone hostile finds the same gaps.
Industries & use cases
LLM application testing matters for any product with an AI feature — SaaS copilots, chatbots, content and code assistants — especially those handling multiple users' data or connected to tools and payments.
What we test
Manual, exploit-driven coverage focused on real impact — not scanner noise.
- Prompt injection and output handling
- Authentication and authorization
- Sensitive-data exposure via the model
- API and integration security
- Traditional web vulnerabilities
- Abuse, cost and rate-limit issues
Signs you should run a llm application penetration testing
If any of these apply, testing is overdue.
- Your product has an AI or LLM feature
- The model connects to tools, data or actions
- Multiple users' data flows through the feature
- You've tested the model or the app, but not together
- You handle sensitive data via AI functionality
Common vulnerabilities we find
The issues that show up again and again in llm application penetration testing — and the ones that actually get exploited.
Prompt injection & output handling
Injection leading to XSS or unsafe downstream actions.
Authorization gaps
Standard access-control bugs in the surrounding app.
Sensitive-data exposure
The model surfacing data the user shouldn't reach.
Insecure output handling downstream
Model output used in queries, commands or markup without validation.
Cost and rate-limit abuse
Unbounded model usage enabling denial-of-wallet and abuse.
Broken access control around AI features
Standard authorization bugs exposing data through the AI layer.
Insecure plugin/tool integrations
Connected tools invoked unsafely by the model.
Sensitive data leakage via the model
The model surfacing data the requesting user shouldn't reach.
Example attack chain
Real impact rarely comes from one bug — it comes from chaining several. A representative example:
Why choose webhook.press
Attacker mindset
We’re offensive security people who also build the free tools bug-bounty hunters use daily. We test the way real adversaries do — and prove it.
No scanner theatre
You get findings a human exploited and verified, ranked by real business risk — not an unfiltered tool dump padded to look thorough.
Fixed price, free retest
A clear quote after scoping, a report your team and auditors can use, and a free retest to confirm the fixes hold.
Our methodology
A structured, transparent process aligned to OWASP and PTES — with a named point of contact throughout.
Scoping & rules of engagement
We agree the exact targets, depth, timing and constraints for your llm application penetration testing, and set clear rules of engagement so testing is safe and predictable.
Reconnaissance & mapping
We enumerate the full attack surface — endpoints, roles, technologies and trust boundaries — and threat-model where an attacker would focus.
Manual testing & exploitation
We attack by hand, verifying and chaining weaknesses into real impact. Automated tooling supports coverage; humans find the logic and authorization bugs it can’t.
Analysis & risk rating
Each finding is validated, rated by real business risk rather than raw scanner severity, and de-duplicated so you get signal, not noise.
Reporting
You receive an executive summary for leadership and a technical section with reproducible PoCs, evidence and concrete, stack-specific remediation.
Free retest
After you remediate, we re-test the closed findings to confirm they are actually fixed — at no extra cost.
What you get
Reproducible PoCs
Every finding ships with a working proof and step-by-step reproduction your engineers can replay and verify.
Risk-ranked findings
Prioritised by real business impact, so you fix what matters first instead of drowning in low-severity noise.
Actionable remediation
Concrete, stack-specific fixes and guidance — not generic “apply best practice” filler.
Free retest
We re-check the closed findings after remediation and confirm they’re resolved.
The report is written for two audiences at once: an executive summary that a non-technical stakeholder can act on, and a detailed technical section — findings, evidence, CVSS and reproduction — that your developers and auditors can use directly.
Manual testing, not just a scan
Automated scanners are fast and useful for coverage, but they only find patterns they already know, and they flood you with false positives. They cannot understand your business logic, chain several small issues into a breach, or tell you which alert actually matters. Our llm application penetration testing is led by experienced testers who exploit findings by hand and rank them by the damage they’d really do — the difference between a 300-page tool dump and a short list of things worth fixing.
Standards & compliance
Our llm application penetration testing reports are written to support the frameworks your auditors and customers care about — including SOC 2, ISO 27001, PCI DSS, HIPAA, GDPR, and the EU’s NIS2 and DORA. Each engagement produces clear, independent evidence of testing with scope, methodology and findings documented the way assessors expect, so the same test both hardens your systems and helps satisfy your obligations.
Frameworks we follow
Recognised methodologies, adapted to your target — never a rigid checklist.
- OWASP — Web Security Testing Guide, ASVS and the API Security Top 10
- PTES — the Penetration Testing Execution Standard
- NIST SP 800-115 — technical guide to security testing
- OSSTMM — Open Source Security Testing Methodology Manual
- MITRE ATT&CK — to frame real-world adversary techniques
FAQ
Is this different from prompt-injection testing?
Broader — prompt injection is one part; here we test the entire application, model and infrastructure together.
Which models/providers?
Any — OpenAI, Anthropic, open-weight models and self-hosted setups.
How is this different from prompt-injection testing?
Broader — prompt injection is one part; here we test the entire application, model and infrastructure together.
Which models and providers?
Any — OpenAI, Anthropic, open-weight and self-hosted deployments.
How is this broader than prompt-injection testing?
It covers the whole application — authorization, integrations, data flows and infrastructure — not just the model's prompt boundary.
Which providers and models?
Any — hosted (OpenAI, Anthropic) and self-hosted or open-weight deployments.
How long does a llm application penetration testing take?
Most focused engagements run one to two weeks depending on scope; we agree the exact timeline during scoping.
Is a retest included?
Yes — after you remediate, we re-check the closed findings to confirm they’re fixed, at no extra cost.
How do you price it?
Per engagement, based on scope and depth — you get a fixed quote after a short scoping call. No cookie-cutter packages.
Why teams trust our llm application penetration testing
Aligned to recognised standards
Testing follows OWASP, PTES, NIST SP 800-115 and MITRE ATT&CK, and reports support SOC 2, ISO 27001, PCI DSS, HIPAA, GDPR, NIS2 and DORA.
Every engagement includes
Reproducible PoCs, risk-ranked findings, concrete remediation, a named point of contact, a free retest, and a summary letter of attestation.
Confidential by default
Work is covered by an NDA, scoped with clear rules of engagement, and data is handled with care throughout — including residency requirements.
Sample report available on request — see our reporting format and depth before you commit. Ask for a sample →
Get a quote for LLM Application Penetration Testing
Tell us about the target — we’ll come back with scope and a fixed price. Manual, exploit-driven testing with reproducible proof and a free retest.