Know whether your AI agents are exploitable — before someone else finds out.
Autonomous agents add a whole new dimension to your attack surface: probabilistic behaviour, tool access, persistent memory, multi-agent communication. We assess your Agentic AI systems against the OWASP Agentic Threats T1–T15 — methodically, evidence-based, with validated exploits.
Agentic AI Pentesting at a glance
Classical pentests do not test what makes Agentic AI dangerous.
A web pentest looks for SQL injection. An API pentest checks authentication. Both assume deterministic behaviour — same input, same output. Agentic AI breaks exactly that assumption: probabilistic reasoning, autonomous tool selection, persistent memory, multi-agent communication.
This creates attack classes no classical pentest covers: prompt injection through trusted data sources, memory poisoning that persists across sessions, tool misuse via manipulated reasoning paths, privilege compromise through agent identity. An Agentic AI pentest is its own discipline — and it decides whether your agent stays a tool or becomes the tool of your attackers.
What is an Agentic AI pentest?
An expert-led offensive security assessment of your AI agents — targeted at the specific attack classes that emerge in autonomous, tool-using, memory-bearing systems.
We test what defines the agent: the LLM (KC1), orchestration (KC2), reasoning (KC3), memory modules (KC4), tool integrations (KC5) and the operational environment (KC6). Every layer has its own weaknesses.
Established frameworks (OWASP Agentic Threats T1–T15, MAESTRO, NIST AI RMF) combined with modern pentest tools (AgentDojo, Agentic Radar, AgentPoison, Garak, Promptfoo) and manual validation — no pure tool reports, no generic checklists.
Every weakness is validated: with a reproducible proof-of-concept, documented attack path and concrete impact. No hypotheses, no theoretical risks — only what is actually exploitable.
OWASP Top 10 for LLM Applications
The 10 most critical security risks for large language models — the foundation of our testing methodology.
Malicious instructions in inputs that manipulate LLM behavior.
Confidential data exposed through outputs or configurations.
Compromised third-party models, datasets, or libraries.
Manipulation of training or fine-tuning data for backdoors.
LLM outputs forwarded to downstream systems without validation.
Too much autonomy for LLM agents — unintended actions.
System prompts are disclosed or inferred.
Attacks on RAG pipelines and embedding databases.
False or misleading information that appears credible.
Excessive resource consumption through uncontrolled inference requests.
What we test
From classic LLM attacks to agentic exploitation scenarios — this is what an AI pentest covers.
When an Agentic AI pentest makes sense
Four typical situations where the factual basis of an Agentic AI pentest makes the difference between a secure and an exploitable system.
How we work.
Four structured phases — from architecture analysis through targeted exploitation to a documented remediation roadmap.
What you get.
Concrete, comprehensible deliverables — no generic compliance documents, no raw tool output.
Not every security assessment answers the same question.
Classical pentesting, LLM red teaming and Agentic AI pentesting complement each other — they don't replace each other.
- OWASP Web Top 10, API Top 10, infrastructure
- Deterministic attacks against known classes
- Answers the where, not the what does the agent do
- Prompt injection, bias, content risks
- Focus on the language model itself
- Answers the model, not the system around it
- End-to-end: LLM + tools + memory + reasoning + multi-agent
- Validated exploit chains against OWASP T1–T15
- Answers the system — and what to do next
From secure AI systems to audit-ready compliance
Classical web vulnerabilities meet AI-specific risks: prompt injection, data and model poisoning, insecure tool and RAG paths. Our pentests and OWASP-aligned reviews deliver reproducible evidence — matching what regulators and auditors expect under "robustness", "cybersecurity" and risk management.
For high-risk AI systems, documented risk analyses and effective technical measures are mandatory. Pentest findings substantiate Art. 15 (cybersecurity, robustness) and strengthen risk management under Art. 9. Transparency and data obligations (Art. 10, 13) can be backed up with clear evidence on data flows, logging and the model supply chain.
- Art. 9 — risk management system: continuous, documented, tied to the risk class
- Art. 10 — data & governance: quality, bias monitoring, representative training and operating data
- Art. 15 — accuracy, robustness, cybersecurity: targeted attack simulations and hard PoCs
AI components in critical and essential sectors are subject to stricter security and evidence requirements. Regular security assessments, vulnerability handling and robust risk artefacts are part of the expected baseline.
- Regular security assessments of the AI infrastructure
- Demonstrable risk artefacts for regulatory conversations
- Integration into NIS2 incident-response processes
The AI management system requires operational security and continuous evaluation. Technical tests (pentest, red team, targeted LLM/agent scenarios) deliver measurable inputs for control, improvement and certification discussions.
- Measurable inputs for the AIMS control system
- Combinable with ISO 27001 for shared evidence
- Foundation for certification discussions and audits
The ICT attack surface grows with every chat interface, copilot and autonomous workflow. DORA requires systematic testing of digital resilience; from the regulator's perspective, the same standards apply to AI-supported systems as to classical IT.
- ICT risk management incl. AI supply chains and outsourcing
- Demonstrable test and review cycles, not just point measures
- Documentable findings for internal audit and regulatory conversations
Frequently asked questions
What distinguishes AI pentesting from traditional pentesting?
AI fundamentally expands the attack surface beyond traditional software. Prompts, context data, data pipelines, and agentic logic become independent risk points. New attack classes like prompt injection, data poisoning, and model extraction have no precedent in traditional security.
What are agentic AI risks?
Agentic AI is not a future topic. We simulate targeted exploitation scenarios against agentic AI architectures: tool misuse & privilege escalation, behavior takeover & memory poisoning, prompt injection in multi-agent workflows, identity abuse (human to agent). Based on OWASP Top 10 for Agentic Applications 2026.
Which compliance requirements does the AI pentest support?
Our tests create robust evidence for EU AI Act (conformity assessment, risk classification), NIS2 (systematic security tests for AI as part of the ICT landscape), DORA (TLPT-ready, BaFin-compliant evidence), GDPR (protection of personal data, 72h breach notification compliance), and ISO 27001 & ISO 42001.
Which frameworks are used?
Traditional security approaches do not work for LLMs. We use three frameworks: OWASP Top 10 for LLM Apps (developer focus), MITRE ATLAS (adversary focus: Recon → Resource Dev → Execution → Exfiltration), and NIST AI RMF (governance focus: GOVERN · MAP · MEASURE · MANAGE).
"Agentic AI pentesting is not a web pentest with a ChatGPT twist. It's a discipline of its own — and it decides whether your agent stays a tool or becomes a tool of your attackers."