What is a Penetration Test for an AI and LLM Application? | Cobalt

Artificial intelligence and large language model (LLM) adoption and cyberattack strategies are outpacing the ability of traditional security testing to keep up. AI and LLM apps expose new vulnerabilities dependent on training models and natural language input, going beyond the static code and structured data risks characteristic of traditional software. Meanwhile AI-powered tools give attackers the ability to increase the scale and frequency of attacks beyond the threshold of traditional defenses.

AI and LLM penetration testing (pentesting) gives organizations new methods and tools capable of meeting today’s security requirements. Here’s an overview of what AI and LLM pentesting is, what it’s for, how it’s done, what challenges it faces, and how it’s becoming a core component of modern security.

What Is Penetration Testing Services for LLM and AI Applications?

AI and LLM pentesting is a specialized cybersecurity assessment that simulates attacks to identify vulnerabilities in AI systems, data pipelines, and model integrations. AI/LLM pentesting is an application of pentesting, an offensive security strategy that preemptively probes apps and networks to find weaknesses, prioritize fixes, and recommend mitigations.

AI and LLM pentesting differ from other types of offensive security tests like vulnerability scanning and red teaming in scope, methodology, and focus. Vulnerability scanning sets the stage for pentesting by using automated tools like Static Application Security Testing (SAST) and Dynamic Application Security Testing (DAST) to identify known risks, but unlike pentesting, it does not go on to exploit these weaknesses using manual and automated methods. In contrast to red teaming, which simulates a realistic attack targeting vulnerabilities a hacker is most likely to exploit, pentesting takes a comprehensive survey of attack surfaces in order to catalog and prioritize vulnerabilities and mitigations. Finally, AI and LLM pentesting focus specifically on probing the vulnerabilities most characteristic of artificial intelligence and large language model applications such as Gen AI.

Pentesting teams take a step-by-step approach to identifying AI and LLM application vulnerabilities. The testing process begins with reconnaissance to gather information on attack surfaces, defenses, and weaknesses. Using this intelligence, pentesters search systems to gain initial access and then escalate privileges to achieve objectives such as exfiltrating data, poisoning model data, disrupting functionality, or hijacking LLM resources (LLMjacking). The process concludes with reports itemizing findings, prioritizing fixes, and recommending remediations. Preliminary tests may precede additional testing using pentesting or other offensive security methods to verify remediations and intercept emerging risks.

How AI and LLM Pentests Differ from Standard Pentests

AI and LLM penetration tests differ from conventional pentests in their focus on vulnerabilities specific to artificial intelligence applications. Where standard pentests test applications for issues such as broken access control, cryptography errors, and SQL injections, AI pentests target model logic, training data, and API behavior.

Additionally, AI and LLM pentests must consider model training data, often requiring a different approach to data than conventional pentests. AI and LLM applications that use natural language processing (NLP) for user input typically handle unstructured data such as text, audio, and images, although machine learning (ML) applications may use both unstructured data and structured data stored in databases and spreadsheets. In contrast, conventional apps place more emphasis on structured data, although they may use unstructured data formats such as NoSQL.

Along with placing more emphasis on unstructured data, AI and LLM pentesting puts more focus on attack methods that directly target data, such as data poisoning and model inversion. Conventional pentesting also tests data, but usually deals with attack methods that indirectly target data by exploiting how data is handled, such as injection attacks that exploit data validation errors.

While AI/LLM pentests differ from conventional pentesting, the two approaches go together. AI and LLM applications typically depend on a digital ecosystem that includes conventional networks, applications, and APIs, so it’s a best practice to pentest these components as well to ensure comprehensive protection.

Key Areas Tested by AI and LLM Pentests

AI and LLM pentesters take a comprehensive approach to vulnerabilities, but focus on risks that have been prioritized by industry leaders, such as the Open Web Application Security Project (OWASP) Top 10 LLM and Gen AI risks and mitigations. Today’s leading vulnerabilities include:

• Prompt injection: malicious behavior caused by user prompts that violate system guardrails
• Sensitive information disclosure: revelation of private data contained in LLMs or their application contexts, such as financial information, personal identifiable information, or health records
• Supply chain vulnerabilities: risks to LLMs from contaminated supply chain models, training data, or deployment platforms
• Data and model poisoning: vulnerabilities, biases, or back doors inherited from pre-training, fine-tuning, or embedding data
• Improper output handling: poor validation, sanitization, and management of LLM outputs passed on to other components and systems
• Excessive agency: allowing users to exploit manipulated, ambiguous, or unpredicted outputs to call prohibited functions or interface in malicious ways with other systems through extensions
• System prompt leakage: disclosure of sensitive data by manipulation of system prompts or instructions
• Vector and embedding leakages: allowing users to inject harmful input, manipulate output, or view sensitive information by exploiting how vectors and embeddings are created, stored, or retrieved
• Misinformation: data breaches, reputation harm, or legal liability from false or deceptive output that seems trustworthy
• Unbounded consumption: excessive and uncontrolled inferences intended to disrupt service, steal financial resources, or exfiltrate intellectual property

AI and LLM pentesting aim to discover these types of vulnerabilities and recommend remediations to prevent attackers from exploiting them.

Common Techniques and Tools for AI and LLM Pentesting

AI and LLM pentesting deploys a variety of techniques and tools in addition to those characteristic of conventional pentesting. These are designed to test model data handling, reasoning, and external integrations. Some of the most important testing methods include:

• Adversarial prompt crafting: Adversarial prompts test whether users can use prompt injections to generate undesired behavior or output. For example, the pentester may embed malicious commands in a document and then instruct an LLM to summarize it to test whether input validation and sanitization safeguards are sufficient.
• Fuzzing model input: Fuzz testing checks whether entering random or unusual input into AI software triggers bugs or vulnerabilities. For instance, to fuzz test a ML image recognition program’s ability to handle photography anomalies like a shaky camera or lighting changes, a pentester might input images with random blurs or glare.
• Output leakage testing: Pentests for output leakage use a variety of methods to test risk of sensitive data exposure. For example, a tester might insert fake API keys into training data and then check whether prompt injections can be used to expose these keys.
• Membership inference attack (MIA) testing: MIA tests check the ability of hackers to determine whether particular data was used in an ML model’s training set. For instance, a pentester might check whether a hacker can infer a customer’s name from a credit card company’s database.
• Model inversion testing: Model inversion tests check whether hackers can reconstruct a model’s training data. For example, a pentester might test whether inputs of face recognition images can be used to reconstruct personally identifiable information.
• Model poisoning simulation: Model poisoning tests investigate the resilience of models against insertion of malicious data samples. For instance, pentesters may train models to distinguish anomalous data points.
• LLM DAST testing: DAST testing checks models for runtime vulnerabilities. For example, DAST tools may be used to check LLM models for vulnerability to prompt injection.
• API endpoint testing: API endpoint testing checks for vulnerabilities in interfaces between AI apps and other apps. For example, a pentester might check whether an LLM database query can be misused to permanently delete a record through a DROP statement.

These techniques are supported by specialized pentesting tools, such as automated DAST scanners and pentesting platforms integrated with vulnerability frameworks.

Why AI and LLM App Pentesting Is Critical for AI Security

Pentesting forms a crucial component of AI and LLM security because it helps pre-empt some of the biggest business risks stemming from AI apps while promoting regulatory compliance. AI and LLM pentesting helps businesses:

• Prevent data exposure that poses risks to company financial, employee and customer data
• Protect against potential disruption of business operations dependent on AI
• Avoid malicious manipulation of model output in production environments
• Promote security maturity by implementing shift-left security throughout the software development lifecycle
• Achieve compliance with regulatory frameworks that require or recommend pentesting

These considerations make pentesting critical for any business that depends on AI or LLM for operations.

Who Performs AI/LLM Penetration Testing?

AI and LLM pentesting is conducted by specialized security researchers with expertise in both offensive security and AI model behavior, who may be recruited from both internal and external talent. Tests may be performed by:

• Cybersecurity researchers: experts who specialize in AI vulnerabilities.
• Ethical hackers: security specialists with AI hacking skills authorized to perform pentests.
• AI governance teams: Testing teams who verify compliance with internal policies and regulatory requirements by interfacing with all relevant departments to check that company AI and LLM usage meets standards.

Pentesting may be conducted by internal or external personnel. Larger AI developers may have dedicated pentesting teams to check models during development and deployment. Organizations without internal AI cybersecurity resources may outsource to security companies or pentesting as a service providers to access external talent and tools. 

Some pentesting providers may specialize in areas such as AI bias, safety, or compliance. Crowdsourcing is another avenue for recruiting pentesters.

AI and LLM Pentesting Challenges and Limitations

The rapid development of today’s AI and LLM technology and attack methods poses challenges for pentesting teams. Some of today’s biggest barriers include:

• Continually changing AI architectures: AI and LLM technology is constantly improving, requiring ongoing continued education to keep up with the latest trends.
• Lack of testing standardization: While researchers such as the Open Web Application Security Project (OWASP) help track LLM and Gen AI risks and mitigations, there is still no standard framework for AI pentesting, posing challenges to evaluating security across different applications.
• Talent gaps: Conducting pentests requires multi-disciplinary skills encompassing AI, cybersecurity, business operations, and domain-specific knowledge, which may exceed in-house resources.
• Access barriers to hosted proprietary AI and LLM models: Pentests conducted with only external access to AI models may face challenges from restricted insights into internal code and data.

While these limitations can be formidable, they often can be resolved through strategic solutions. Soliciting the expertise of experienced AI pentesting as a service providers can close talent gaps and afford access to experts familiar with the latest technology trends. Arranging for an internal contact to coordinate with pentesting teams can remove access barriers.

The Future of AI Penetration Testing

AI pentesting is undergoing ongoing adaptation to keep pace with growing demand for LLM and Gen AI applications and ever-changing attack tactics. Currently a niche within offensive security, AI pentesting is moving toward standardization and incorporation into AI risk management framework requirements, driven by regulation, model transparency needs, and responsible AI initiatives. Both global and US regulatory authorities have recently promoted standardizing AI pentesting and security testing requirements. Meanwhile, AI-powered automation is transforming pentesting practices, promoting usage of advanced tools to help teams conduct tests more efficiently and replicate attacks at scale. These developments are promoting closer coordination between regulatory requirements, security frameworks, threat intelligence, and pentesting teams.

To stay current on the latest development in AI and IT security, visit Cobalt’s learning center, where you can find more articles to help you learn cybersecurity essentials and fortify your cyber defenses.