LLM Pentesting: Why and How to Test Large Language Models for Security Risks

Large language models (LLMs) are the foundation of Generative AI technology and applications. These models are trained on massive amounts of textual data, which allows them to understand and generate human language for a wide variety of applications, including chatbots, content generation, coding, and document summarization.

But for all their usefulness and growing ubiquity, LLMs also have a flip side: they are critical attack vectors that expand your organization’s attack surface and significantly increase your risk of cyberattacks. Threat actors can use numerous exploitation techniques, including jailbreaking, model data poisoning, and agent-based attacks, to compromise LLMs. They can then force the AI system to produce unexpected, misleading, or harmful outputs. LLM compromise may also allow them to steal sensitive data or gain full control over enterprise systems.

To minimize the risk of LLM exploitation and avoid its (many) undesirable consequences, it’s crucial to proactively test LLMs for security risks.

Enter LLM pentesting.

What is LLM Pentesting?

LLM pentesting is a security exercise that focuses on finding security vulnerabilities in LLMs. Testers aim to discover the weaknesses in LLM prompts, outputs, or integrations that real attackers could misuse to harm the organization and/or LLM users. Early vulnerability discovery is critical for early vulnerability remediation. This then ensures secure LLM deployment and reduces an organization’s susceptibility to cyberattacks and data breaches.

In general, proactive, comprehensive, and continuous LLM penetration testing provides the following benefits to organizations:

• Early detection of security flaws unique to LLMs: These flaws may remain hidden during normal production operations, adversely affecting LLM output and user experiences.
• Safeguard sensitive data: SIdentifying security weaknesses early can help to safeguard sensitive data, including customer PII, proprietary business information, and intellectual property, from unauthorized access, compromise, and theft.
• Implement security measures to maintain AI system integrity: Pentesters simulate various attack scenarios to highlight how malicious adversaries could exploit LLM vulnerabilities in the real world. Security teams can then prioritize and deploy strong defenses to eliminate these weaknesses and maintain system integrity.
• Compliance management: By implementing the recommendations of LLM pentesters, organizations can demonstrate compliance with applicable regulations and minimize the risks of non-compliance.

How LLM Pentesting Works

Some of the most critical vulnerabilities in LLMs, as listed in the “OWASP Top 10 Risk & Mitigations for LLMs and Gen AI Apps” include:¹

• Prompt injections
• Sensitive information disclosure
• Data and model poisoning
• Improper output handling
• Excessive agency
• System prompt leakage

To identify and unpack these and other vulnerabilities, LLM pentesters follow a structured process with the following steps:

1. Define the Testing Scope

Testers outline the purpose of the assessment, including identifying vulnerabilities and assessing potential misuse risks. They specify which aspects of the system will be tested, such as the model, APIs, and integration points. This is also where they establish rules of engagement to define acceptable prompts and data usage limitations.

2. Conduct Reconnaissance and Gather Information

Before actual testing begins, the pen testers interact with the LLM under normal conditions. This allows them to:

• Understand the system’s overall architecture, including how the LLM is integrated with other components.
• Examine any available API documentation, model cards, or usage guidelines.
• Determine the specific LLM being used, its version, and any known characteristics or limitations.

The goal here is to understand the LLM’s default behavior to help identify deviations and anomalies later.

3. Simulate Attacks and Chain Exploits

Pen testers map the LLM’s attack surface, including user inputs and system prompts. Then, they attempt to attack the LLM using various TTPs to identify critical and common issues. This is also when they’ll attempt to override system instructions and make the LLM call unauthorized tools. Some testers also combine or “chain” multiple weaknesses to identify realistic LLM exploitation paths.

4. Evaluate Security Controls

This is the phase where pentesters actively hunt for real vulnerabilities and test the LLM’s resilience against exploitation attempts. Here, they test for prompt injections and attempt to extract sensitive information via malicious inputs. They also evaluate model evasion techniques used to bypass security filters and check for vulnerabilities like rate limiting, resource exhaustion, or authentication bypass.

5. Report Findings

The process concludes with a detailed report. Findings are ranked based on business risk, covering both traditional vulnerabilities and LLM-specific threats. Testers provide actionable remediation recommendations, such as prompt engineering adjustments, model fine-tuning, or integration improvements, to help defenders build resilience quickly.

LLM Pentesting Frameworks

Many frameworks are available to help testers perform LLM pentests. These include:

MITRE ATLAS

MITRE ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems) is a detailed knowledgebase of adversary tactics and techniques aimed at attacking AI systems.² The repository is based on real-world attack observations. This enables pentesters to understand real-world adversary behaviors. They can then emulate threats from an attacker’s perspective and suggest appropriate risk mitigation pathways.

OWASP Top 10 Risks & Mitigations for LLMs and OWASP AI Testing Guide

The OWASP Top 10 List for LLMs covers the 10 most critical vulnerabilities affecting LLMs.¹ It provides a good starting point for LLM pentesting. Focusing on these vulnerabilities enables organizations to develop and secure LLMs and AI systems.

OWASP has also developed an AI testing guide.³ This resource provides a standardized methodology and repeatable test cases to help pentesters evaluate vulnerabilities, biases, and performance degradations in LLM-based systems. Software developers can also leverage the guide to mitigate these issues and deploy more secure AI systems into production.

Deploy Secure LLMs with BreachLock’s LLM Pentesting Services

LLM-based AI systems introduce numerous complex vulnerabilities into your IT ecosystem. Traditional pentesting cannot navigate these complexities, much less enhance the security of AI tools and applications.

To secure these systems, organizations need specialized pentesting that effectively mimics real-world attacks. BreachLock provides human-led and AI-powered solutions to navigate these complexities:

• Penetration Testing as a Service (PTaaS): 100% in-house human-led, AI-accelerated pentesting that aligns closely with industry-leading LLM penetration testing frameworks.
• Adversarial Exposure Validation(AEV): For agentic AI-powered autonomous penetration testing to find vulnerabilities at scale.
• Continuous Threat Exposure Management (CTEM): For continuous attack surface discovery and prioritization as models and integrations evolve.

  As AI risks evolve, your testing strategy should too. Reach out to BreachLock to see how our expert-led services and solutions can help your organization harden your models against real-world exploits.

  References

  1. OWASP (2025). 2025 Top 10 Risk & Mitigations for LLMs and Gen AI Apps. https://genai.owasp.org/llm-top-10/

  2. MITRE (2026). ATLAS Matrix. https://atlas.mitre.org/matrices/ATLAS

  3. OWASP (2025). AI Testing Guide. https://github.com/OWASP/www-project-ai-testing-guide/blob/5c6d357e2290e8c81ab7e6673950e978e1b83604/PDFGenerator/V1.0/OWASP-AI-Testing-Guide-v1.pdf

  About BreachLock

  BreachLock is a global leader in offensive security, delivering scalable and continuous security testing. Trusted by global enterprises, BreachLock provides human-led and AI-powered Attack Surface Management, Penetration Testing as a Service (PTaaS), Red Teaming, and Adversarial Exposure Validation (AEV) solutions that help security teams stay ahead of adversaries.

  With a mission to make proactive security the new standard, BreachLock is shaping the future of cybersecurity through automation, data-driven intelligence, and expert-driven execution.

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