AI & LLM Penetration Testing
Your AI systems, tested like an attacker would.
We test your deployed AI systems adversarially (prompt injection, jailbreaking, agent manipulation, data extraction) using the same attacker mindset we bring to every engagement.
Overview
AI/LLM penetration testing is adversarial testing of your deployed AI: we attack the running system the way a real adversary would. This is distinct from an AI security architecture review (which examines design and data flows) and an AI governance gap assessment (which checks program maturity). If your organization deploys chatbots, copilots, or agents, those systems can be probed and exploited. We attempt prompt injection, jailbreak guardrails, extract training and system data, and abuse agent tool access to prove what an attacker could actually do.
What We Test
We target six categories of AI vulnerability, each requiring different techniques than traditional application testing.
Prompt Injection
Direct and indirect injection attacks to override system instructions, extract system prompts, or manipulate model behavior through crafted inputs.
Jailbreaking & Guardrail Bypass
Attempts to circumvent safety filters, content restrictions, and behavioral constraints through adversarial prompt techniques.
Data Extraction
Testing whether the model leaks training data, system prompts, RAG corpus content, or other sensitive information through carefully constructed queries.
Agent Tool Misuse
For AI agents with tool access: attempts to manipulate the agent into misusing its connected tools, making unauthorized API calls, accessing restricted data, or taking unintended actions.
Output Handling
Testing how model outputs are consumed by downstream systems. Can manipulated outputs trigger XSS, SQL injection, command execution, or other vulnerabilities in connected applications?
Supply Chain & Plugin Security
Evaluating third-party plugins, skills, extensions, and model providers for supply chain risks: malicious packages, insecure integrations, and unverified dependencies.
Our Approach
We adapt our proven penetration testing methodology to AI-specific attack surfaces, combining automated tooling with manual adversarial techniques that require human creativity.
Reconnaissance
We map the AI system's capabilities, connected tools, data sources, and guardrails. Understanding what the system can do tells us what an attacker will try to make it do.
Adversarial Probing
Systematic testing of injection vectors, guardrail boundaries, and data extraction techniques using both known attack patterns and novel approaches.
Exploitation
Successful probes are developed into full exploit chains demonstrating real-world impact: data theft, unauthorized actions, system compromise.
Documentation
Every finding documented with reproduction steps, evidence, severity assessment mapped to OWASP AI categories, and specific remediation guidance.
Common Findings
These are issues we frequently discover during ai & llm penetration testing engagements:
Successful Prompt Injection
HighSystem instructions overridden through crafted inputs, allowing attackers to change model behavior and bypass intended restrictions.
Training Data Leakage
HighModel responses revealing training data, RAG corpus content, or internal information not intended for external users.
Agent Over-Permissioning
CriticalAI agents with access to tools and systems far beyond what their intended function requires, enabling high-impact exploitation if manipulated.
Guardrail Bypass
MediumSafety filters and content restrictions circumvented through adversarial techniques, allowing generation of prohibited content or actions.
Unvalidated Output Handling
HighModel outputs passed directly to downstream systems without sanitization, enabling injection attacks through the AI as an intermediary.
Insecure Plugin Integrations
HighThird-party plugins or extensions running with excessive permissions, lacking integrity verification, or pulling from unvetted sources.
Common Questions
How is this different from a regular application pentest?
Can you test third-party AI as well as custom-built?
What frameworks do you test against?
Do we need a staging environment for AI testing?
Other Penetration Testing Options
External Penetration Testing
We attack your perimeter the way real adversaries would: scanning for exposed services, testing authentication mechanisms, and attempting to breach your internet-facing systems.
Internal Penetration Testing
Simulating a compromised workstation or rogue insider, we test how far an attacker could move laterally through your network and what sensitive data they could access.
Wireless Security Testing
We assess your wireless networks for rogue access points, weak encryption, and attack vectors that could give adversaries a foothold into your environment.
Physical Penetration Testing
Combining physical security testing with social engineering, we evaluate whether attackers could gain physical access to sensitive areas and systems.
Assumed Breach Testing
Skip the initial access phase and focus testing on post-compromise objectives. We start with simulated access and demonstrate what an attacker could accomplish once inside your environment.
Ready to Strengthen Your Defenses?
Schedule a free consultation with our security experts to discuss your organization's needs.
Or call us directly at (445) 273-2873