AI penetration testing
AI penetration testing probes how production AI systems behave when adversaries attempt prompt injection, tool misuse, and data exfiltration.
This guide covers seven AI attack categories (prompt injection, PHI extraction, jailbreaking, role confusion, trust escalation, output manipulation, and context poisoning), with findings mapped to MITRE ATLAS and OWASP LLM Top 10.
What is AI penetration testing?
AI penetration testing is the practice of systematically probing an AI system with adversarial inputs to discover vulnerabilities before real attackers exploit them. It’s the AI equivalent of a network pen test, but instead of scanning ports and testing firewalls, you’re probing the model’s behavioral boundaries.
The goal isn’t to break the model for sport. It’s to understand what happens when someone tries, and to produce evidence that you tested, documented, and addressed the results. That evidence matters for compliance under the EU AI Act, NIST AI RMF, and state-level regulations like the Colorado AI Act.
Common vulnerability classes
AI systems face a distinct set of attack vectors that don’t map neatly to traditional application security. These are the seven categories that matter most:
1. Prompt injection
ATLAS AML.T0051Adversarial input that overrides system instructions. Direct injection sends malicious prompts through the user interface. Indirect injection embeds malicious instructions in data the model retrieves, such as documents, web pages, or database records. This is the most common attack vector against production LLMs.
2. PII extraction
ATLAS AML.T0024Conversational techniques that coax the model into revealing personally identifiable information from its training data, retrieval context, or conversation history. Multi-turn attacks are particularly effective. They build rapport over several exchanges before asking for sensitive data.
3. Role confusion
OVERT RT-2Prompts that cause the model to abandon its assigned persona or role constraints. A customer service bot that starts giving legal advice or a clinical AI that acts as an emergency dispatcher. Role confusion often emerges gradually over extended conversations rather than from a single prompt.
4. Trust-building escalation
OVERT RT-4Multi-turn attacks can establish benign context before escalating requests. Scoped studies show that extended interaction can erode some single-turn defenses, but results depend on the exact model, version, system prompt, defense, protocol, grader, and turn budget.
5. Jailbreaking
ATLAS AML.T0054Techniques that bypass the model’s safety guardrails entirely. DAN-style attacks, encoding tricks, multilingual exploits, and persona-based circumvention. While providers continuously patch known jailbreaks, new variants emerge faster than defenses. Single-turn defense rates don’t predict multi-turn resilience.
6. Output manipulation
OVERT RT-5Attacks that steer the model into generating harmful, biased, or factually incorrect output while appearing to follow its guidelines. Subtle framing, leading questions, and contextual priming can produce outputs that violate safety policies without triggering standard content filters.
7. Context poisoning
ATLAS AML.T0049Exploiting retrieval-augmented generation (RAG) by planting malicious content in documents the model retrieves. When the model trusts its retrieval context, poisoned documents can override system instructions, inject false information, or redirect behavior, all without the attacker directly interacting with the model.
How to run an AI penetration test
A structured AI pen test follows five steps:
Step 1: define the target
Identify the model endpoint, the system prompt, and any retrieval or tool-use integrations. Document the model’s intended behavior, safety boundaries, and the data it can access. This is your baseline.
Step 2: run automated probes
Use an adversarial evaluation tool to exercise the categories relevant to the workflow. autoredteam runs a configurable probe set, including multi-turn scenarios; interpret findings within the tested model, prompts, tools, judge, and date.
Step 3: analyze findings
Assess each finding for severity (how dangerous is this vulnerability?), exploitability (how easy is it for a real attacker to trigger?), and impact (what happens if this is exploited in production?). Not every finding requires immediate action, but every finding needs classification.
Step 4: map to frameworks
Cross-reference findings to MITRE ATLAS techniques, relevant NIST AI RMF functions, and OVERT evidence fields where useful. The mapping helps reviewers navigate a report; it does not turn a test result into compliance evidence or establish that a control requirement is satisfied.
Step 5: schedule recurring tests
A single pen test is a snapshot. Define recurring or change-triggered tests based on the workflow, model, prompt, tool, and dependency risks. The appropriate interval is system-specific; document the cadence, triggers, exclusions, and tested configuration.
Framework mapping
Every attack category maps to specific controls in the governance frameworks that auditors and regulators reference:
| Attack category | MITRE ATLAS | OVERT | NIST AI RMF |
|---|---|---|---|
| Prompt injection | AML.T0051 | RT-3 | Measure 2.6 |
| PII extraction | AML.T0024 | RT-1 | Govern 1.5 |
| Role confusion | AML.T0043 | RT-2 | Map 1.5 |
| Trust escalation | AML.T0040 | RT-4 | Measure 2.7 |
| Jailbreaking | AML.T0054 | RT-3 | Measure 2.6 |
| Output manipulation | AML.T0048 | RT-5 | Measure 2.5 |
| Context poisoning | AML.T0049 | RT-6 | Manage 2.4 |
Explore further
AI runtime security
Why pre-deployment testing is a snapshot and how continuous monitoring works.
AI agent security
Why agentic systems break traditional AppSec, and how to test tool-use chains.
Prompt injection
Attack taxonomy, defense strategies, and why ongoing monitoring complements point-in-time testing.
OWASP LLM Top 10
The ten LLM application risks every penetration test should cover.
See it in action
Run a configurable open-source adversarial assessment, record the tested scope, and map supported findings to the risk framework your team actually uses.