OWASP Top 10 LLM Risks Explained

Jeff Crume, a Distinguished Engineer at IBM, outlines the critical security risks associated with Large Language Models (LLMs) in a recent video, drawing parallels to the established OWASP Top 10 for Web Applications. The discussion focuses on how easily LLMs can be manipulated to leak sensitive information or perform unintended actions, posing significant threats to organizations deploying these powerful AI systems.

Understanding the LLM Security Landscape

Crume begins by emphasizing the alarming ease with which LLMs can be compromised. A cleverly crafted prompt, an exposed training file, or a malicious plugin can all lead to security incidents, steering the LLM to reveal information it shouldn't or execute actions the user never intended. This highlights a fundamental challenge: LLMs, while powerful, are not inherently secure and require careful consideration during deployment.

The OWASP Top 10 for LLM Applications

The OWASP community, known for its work in web application security, has extended its focus to LLMs, releasing a "Top 10 for LLM Applications" list. This list aims to guide developers and organizations in identifying and mitigating the most common and critical security threats. Crume walks through several of these key vulnerabilities:

The full discussion can be found on IBM's YouTube channel.

• 1. Prompt Injection: This is the most prevalent threat, where an attacker manipulates the LLM by injecting malicious prompts. This can override the system's original instructions, leading to unintended actions like data leakage or the execution of harmful commands. Crume illustrates this with the example of an LLM being asked to generate instructions for building a bomb, bypassing its safety protocols.
• 2. Sensitive Information Disclosure: LLMs can inadvertently reveal sensitive data they were trained on or have access to. This can occur through direct prompting or as a side effect of other attacks, potentially exposing proprietary information, personal data (PII), or health information (PHI).
• 3. Supply Chain Vulnerabilities: The reliance on third-party LLMs, datasets, or tools creates a significant supply chain risk. If any component in the supply chain is compromised, it can introduce vulnerabilities into the deployed LLM application.
• 4. Data/Model Poisoning: Attackers can deliberately corrupt the training data or the model itself. This poisoning can introduce biases, backdoors, or cause the LLM to generate incorrect or harmful outputs, undermining its integrity.
• 5. Improper Output Handling: When an LLM's output is not properly validated or sanitized before being used by other systems, it can lead to vulnerabilities like Cross-Site Scripting (XSS), SQL injection, or arbitrary code execution.
• 6. Excessive Agency: Granting an LLM too much power or agency to interact with external systems without proper oversight can be dangerous. If an LLM can execute commands or access sensitive APIs without strict controls, it can be exploited to cause significant damage.
• 7. System Prompt Leakage: The system prompt, which guides the LLM's behavior, can sometimes be leaked through clever prompting, revealing the underlying instructions and potentially allowing attackers to manipulate the LLM more effectively.
• 8. Vector/Embeddings Weaknesses: In the context of retrieval-augmented generation (RAG) systems, vulnerabilities in how data is vectorized and retrieved can be exploited. If the retrieved data is compromised or manipulated, it can lead to the LLM generating inaccurate or malicious responses.
• 9. Misinformation/Misleading LLM Outputs: LLMs can sometimes generate plausible-sounding but incorrect or fabricated information, a phenomenon known as hallucination. This can be exacerbated by poisoned data or models, leading users to trust and act upon false information.
• 10. Unbounded Consumption: If an LLM application is not properly rate-limited or managed, it can be susceptible to denial-of-service (DoS) attacks, where attackers flood the system with requests, consuming excessive resources and making it unavailable to legitimate users.

Defense Strategies for LLM Security

Crume emphasizes that the security of LLM applications requires a multi-layered approach. Key defense strategies include:

• Sanitize Data: Rigorously clean and validate all data used for training and prompt augmentation to prevent the introduction of malicious content.
• Access Controls: Implement strict access controls to limit who can interact with the LLM and what actions it is permitted to perform. This includes ensuring that only authorized users can access sensitive data or trigger potentially harmful operations.
• Mitigate Misconfigurations: Many LLM vulnerabilities stem from misconfigured systems or models. Ensuring proper security settings and adhering to best practices are crucial.
• Vet Data/Suppliers: Scrutinize the sources of training data and the LLM models themselves, especially when using third-party or open-source options. Understand the provenance and integrity of these components.
• Scan and Red Test: Regularly scan the LLM system for known vulnerabilities and conduct red team exercises to proactively identify and address potential attack vectors.
• Patching: Keep the LLM models and the underlying infrastructure updated with the latest security patches to protect against known exploits.
• Source Verification: Understand the origin of the data and models used, ensuring their trustworthiness.
• Input Validation: Implement robust input validation to detect and block malicious prompts, similar to how web applications handle user input.
• Output Validation: Similarly, validate and sanitize the LLM's output before it is used by downstream systems or presented to users.
• Monitoring and Logging: Implement comprehensive monitoring and logging to detect suspicious activity and enable rapid incident response.

By understanding these risks and implementing robust security measures, organizations can better harness the power of LLMs while mitigating the potential for misuse and harm.