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Agentic AI Penetration Testing · Offensive AI Security

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.

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OWASP Agentic AI methodologyValidated exploitable findingsISO 42001 & EU AI Act ready
Methodical
OWASP Agentic Threats T1–T15 · MAESTRO · NIST AI RMF
End-to-end
LLM · Tools · Memory · Reasoning · Multi-agent
Evidence-based
Validated exploits with PoC, not theory
Compliance
ISO 42001 · EU AI Act · NIS2 · DORA
The problem

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.

Definition

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.

Focus on agent components

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.

Methodology & tools

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.

Evidence-based findings

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.

Use cases

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.

Before production deployment
Before an agent system goes into production — and gains access to customer data, internal systems or critical workflows. Validate the security controls under real attack conditions.
ISO 42001 & EU AI Act conformity
High-risk AI systems require documented security assessments. A structured Agentic AI pentest delivers the solid evidence auditors and regulators expect.
After architecture changes
New tools, MCP servers, additional agents, expanded memory stores — every extension changes the attack surface. Re-tests confirm the original security posture is preserved.
M&A & cybersecurity due diligence
When acquiring AI-enabled products or platforms: technical assessment of whether the acquired Agentic AI is safe to integrate — or whether hidden weaknesses constitute an acquisition risk.
Approach

How we work.

Four structured phases — from architecture analysis through targeted exploitation to a documented remediation roadmap.

1
Scoping & threat modeling
Understand the architecture, identify components, define trust boundaries. Threat model based on OWASP Agentic Threats and MAESTRO.
Frameworks: OWASP Agentic Threats T1–T15 · MAESTRO Layered Threat Model · NIST AI RMF
2
Recon & component mapping
Enumerate KC1–KC6 components: LLM, orchestration, reasoning, memory, tools, operational environment. Document the attack surface per layer.
Tools: Agentic Radar · manual component inventory · architecture analysis
3
Exploitation & validation
Targeted attacks against T1–T15: prompt injection, memory poisoning, tool misuse, privilege compromise, multi-agent hijacking. Manual validation of every finding.
Tools: AgentDojo · AgentPoison · Garak · PyRIT · Promptfoo · ASB
4
Reporting & remediation
Risk-based prioritisation, documented PoCs, concrete measures — directly actionable by your team. Re-test included.
Output: Threat model · Findings · Remediation roadmap
Deliverables

What you get.

Concrete, comprehensible deliverables — no generic compliance documents, no raw tool output.

Threat model (MAESTRO / OWASP)
Documented architecture of your agent system with trust boundaries, component mapping and a layer-specific threat landscape.
Validated exploitable findings
Every weakness with a reproducible proof-of-concept, full attack path and concrete impact rating — no theoretical risks.
Risk mapping T1–T15
Identified weaknesses mapped to the OWASP Agentic Threats — directly usable for ISO 42001 risk management and EU AI Act conformity assessment.
Remediation roadmap & re-test
Risk-prioritised measures with concrete technical recommendations. Re-test after remediation — as verification and audit evidence.
Positioning

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.

Classical weaknesses
Classical pentest
"Where are the classical weaknesses in web, API, infrastructure?"
  • OWASP Web Top 10, API Top 10, infrastructure
  • Deterministic attacks against known classes
  • Answers the where, not the what does the agent do
Model behaviour
LLM red teaming
"Can the model be jailbroken or pushed to undesired output?"
  • Prompt injection, bias, content risks
  • Focus on the language model itself
  • Answers the model, not the system around it
Full attack chain
Agentic AI pentesting
"Can the agent be misused as a system — across all components?"
  • End-to-end: LLM + tools + memory + reasoning + multi-agent
  • Validated exploit chains against OWASP T1–T15
  • Answers the system — and what to do next
Valeri Milke — Founder & CEO VamiSec GmbH
Valeri MilkeFounder & CEO · VamiSec GmbH
Your contact

"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."

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