AI & LLM Penetration Testing
AI penetration testing is a hands-on security assessment of AI- and LLM-powered systems that simulates real attacks — prompt injection, jailbreaks, system-prompt and training-data exfiltration, and model denial-of-service — mapped to the OWASP LLM Top 10. Berghem manually tests your models, agents and RAG pipelines, then delivers prioritized, exploit-proven remediation.
LLM Penetration Testing Methodology
Four phases that walk your models, agents and RAG pipelines through the OWASP LLM Top 10 — LLM01 to LLM10 — producing reproducible evidence at every step.
Need to validate detection and response as well? See Red Team for AI, the full adversarial campaign that chains the flaws found in the pentest all the way to the attacker's objective.
Map the model's surface
Endpoints, system prompts, plugins, RAG, and data flow — documented before the first test.
surface_map.jsonTest the ten categories
Prompt injection, output handling, data poisoning, model DoS — systematic and reproducible coverage.
findings.md · 10/10Audit the stack that supports the model
Gateways, model serving, vector DBs, secrets, and isolation — where most critical flaws live.
stack_audit.logTranslate findings into decisions
Severity, exploitability, and business impact in a matrix that prioritizes what to fix first.
risk_matrix.csvWhat does the OWASP LLM Top 10 cover?
Each category is exercised with real techniques observed in 2025–26 incidents, with reproducible PoC and AI-VRM risk classification.
Prompt Injection
Direct and indirect attacks that manipulate behavior, bypass guardrails, or trigger unintended actions.
Insecure Output Handling
Validation flaws leading to XSS, SSRF, code execution, or escalation in downstream systems.
Training Poisoning
Manipulation of pre-training, fine-tuning, or embedding data to introduce biases or backdoors.
Model Denial of Service
Intensive operations that degrade performance, inflate costs, or disrupt service.
Supply Chain
Pre-trained models, datasets, plugins, and third-party extensions that introduce vulnerabilities.
Sensitive Data Disclosure
Exposure of PII, secrets, or system prompts via responses or side channels.
Insecure Plugin Design
Tool integrations that allow unauthorized actions or code execution.
Excessive Agency
Systems with permissions or autonomy beyond the intended scope — exploitable for arbitrary actions.
Overreliance
Trusting LLM output without validation — misinformation, vulnerabilities, incorrect decisions.
Model Theft
Unauthorized extraction, replication, or exfiltration of proprietary models and weights.
What You Receive
No generic report. Every deliverable is addressed to the right audience — from the board to the engineer.
Board summary
Business impact analysis, top risks, and financial exposure — in 2 pages, C-suite language.
board_summary.pdfFindings with PoC
Each vulnerability with reproducible proof-of-concept, request/response logs, and CVSS+AI-VRM classification.
findings_full.mdAI-VRM Matrix
Severity × exploitability × impact, with P0/P1/P2 prioritization and suggested SLA per category.
risk_matrix.csv30/60/90 Plan
Concrete actions per finding, with owner, effort, and window. Includes a readout workshop with your team.
remediation_plan.xlsxAI Attack Techniques We Test
We reproduce the techniques seen in real 2025–26 incidents against your models, agents and RAG pipelines — not a checklist scan.
Direct & indirect prompt injection — Overriding instructions through user input or through poisoned content the model retrieves from documents, tools or RAG sources.
Jailbreaks & guardrail bypass — Role-play, encoding and multi-turn techniques that defeat safety filters and system-prompt constraints.
System-prompt & training-data exfiltration — Extracting hidden instructions, secrets and memorized data from responses and side channels.
Model denial-of-service — Resource-exhaustion and cost-amplification prompts that degrade availability or inflate inference bills.
Insecure output handling & tool abuse — Chaining model output into XSS, SSRF or code execution in downstream systems and plugin integrations.
Excessive agency & RAG poisoning — Abusing over-permissioned agents and poisoned knowledge bases to trigger unintended, high-impact actions.
Traditional pentest vs AI pentest
A traditional pentest and an AI pentest share the adversarial method but diverge in target, failure classes and success criteria. The table below summarizes the differences that change how you plan, run and repeat the test.
| Dimension | Traditional pentest | AI pentest |
|---|---|---|
| Target | Web applications, APIs, networks and infrastructure | Models and LLMs, RAG pipelines, tool-using agents and the stack that serves them |
| Failure classes | OWASP Top 10 web: SQL injection, XSS, broken access control, SSRF | OWASP LLM Top 10: prompt injection, jailbreak, data leakage, excessive agency |
| Tooling | Scanners, proxies, exploits and deterministic fuzzers | Prompt payloads, jailbreak harnesses, embedding analysis and semantic fuzzing |
| Non-deterministic criterion | Deterministic behavior: the flaw reproduces identically every run | The same input can yield different responses, requiring sampling and repetition to confirm the flaw |
| Retest frequency | After fixes and significant release changes | On every change of model, system prompt, RAG knowledge base or agent tool set |
Manual pentest vs automated AI red teaming
Automated tools scale coverage of known patterns; manual testing finds the business-logic and chained flaws that matter. Berghem combines both, led by humans.
| Dimension | Berghem manual pentest | Automated AI red teaming |
|---|---|---|
| Coverage | Deep, context-aware testing of your specific models, agents and data flows | Broad, fast scanning of known prompt-injection and jailbreak patterns |
| Business-logic & chained flaws | Found — testers chain weaknesses to a real objective | Rarely found — limited to predefined signatures |
| False positives | Triaged and validated by hand | Higher — needs human review |
| Exploit proof | Reproducible proof-of-concept per finding | Often flags without a working exploit |
| Remediation guidance | Prioritized, tailored 30/60/90 plan | Generic recommendations |
| Compliance evidence | Mapped to OWASP LLM Top 10 and AI governance frameworks | Partial, tooling-dependent |
How AI pentesting supports AI governance
Findings are mapped to the frameworks your auditors and regulators expect, so a pentest doubles as evidence for your AI governance program.
EU AI Act — Evidence of adversarial testing and risk management for high-risk AI systems placed on the EU market — supported in Europe through Berilo.
ISO/IEC 42001 — Demonstrable controls for the AI management system, including testing, monitoring and continual improvement of AI risks.
NIST AI RMF — Coverage of the Measure and Manage functions with concrete, exploit-proven evidence of AI risks and mitigations.
AI pentest vs AI Red Team
An AI pentest pursues breadth: systematic, reproducible coverage of the ten OWASP LLM Top 10 categories across the whole system surface, with a prioritized list of vulnerabilities. An AI Red Team pursues depth: a business-goal-driven adversarial campaign that chains flaws to final impact and measures whether the defense detects and responds to the attack.
The two complement each other — use the pentest to map where you are vulnerable and the AI Red Team to validate that your detection and response controls hold under a real, chained attack.
Pentesting with AI: Berghem Agents
Berghem also applies artificial intelligence to its own offensive process, combining human operators with autonomous security agents.
The Berghem Agents automate reconnaissance, payload generation and mutation, and finding triage to widen pentest coverage — always under the supervision of human specialists who validate every vulnerability and discard false positives. This lets us exercise more attack variations in less time, without giving up the reproducible proof of concept and prioritized remediation plan that define a Berghem AI pentest.
AI/LLM pentest — frequently asked questions
What is AI penetration testing?
How is it different from traditional application pentesting?
How long does an AI pentest take and what does it cost?
What standards does the assessment follow?
What do we receive at the end?
What is the difference between an AI/LLM pentest and AI Red Team?
Ready to test your AI systems before attackers do?
Schedule an AI/LLM pentest and get a full view of your attack surface — from prompt injection to model theft.
Request AI pentest