Large Language Models (LLMs) like GPT-4 have revolutionized the way businesses operate, bringing advanced AI capabilities to industries ranging from healthcare to finance. However, the increasing reliance on LLMs has introduced unique security challenges that organizations cannot afford to ignore. Recognizing these emerging risks, the Open Web Application Security Project (OWASP) developed the Top 10 vulnerabilities for LLMs, a guide to help organizations secure these powerful systems.
To address these challenges, AppSOC provides integrated support for OWASP’s framework. By leveraging AppSOC’s tools, organizations can identify, track, and mitigate the vulnerabilities outlined in the OWASP Top 10 for LLM Applications, ensuring safe, compliant, and effective AI deployment.
Following summary of each of these risks and the recommended mitigation steps to ensure the security and integrity of LLMs. These have been updated for the 2025 version released in November, 2024.
LLM01: Prompt Injection
- Definition: Malicious actors manipulate an LLM’s input to alter its behavior, potentially leading to unauthorized actions or leakage of sensitive information.
- Explanation: Prompt injection remains a critical issue, now divided into direct and indirect forms. Attackers exploit weaknesses in input validation to bypass security controls, gain unauthorized access, or override system instructions.
- Recommendations: Use robust prompt filtering, input validation, and context-aware prompt handling to detect and neutralize injection attempts.
LLM02: Sensitive Information Disclosure
- Definition: LLMs unintentionally expose sensitive data such as personally identifiable information (PII), financial records, or security credentials.
- Explanation: Improper handling of training data and response generation can cause LLMs to leak sensitive information. Models trained on improperly sanitized data may inadvertently regurgitate confidential information.
- Recommendations: Implement strong access controls, encrypt sensitive data, use differential privacy techniques, and deploy robust content filtering.
LLM03: Supply Chain Vulnerabilities
- Definition: Third-party components, including pre-trained models and datasets, introduce security risks if compromised or manipulated.
- Explanation: Dependency on open-source or third-party LLM models increases risks of poisoning attacks, biased outputs, and system failures due to unverified data sources.
- Recommendations: Vet external components thoroughly, ensure cryptographic integrity of datasets, and establish a robust monitoring pipeline.
LLM04: Data and Model Poisoning
- Definition: Attackers introduce malicious data into training sets or fine-tuned models to influence LLM behavior.
- Explanation: Poisoned datasets can introduce biases, degrade model performance, or create backdoors that can be exploited post-deployment.
- Recommendations: Use anomaly detection for data integrity, apply secure training pipelines, and monitor for model drifts.
LLM05: Improper Output Handling
- Definition: LLM-generated content is not properly validated before being used by downstream applications.
- Explanation: Failing to sanitize LLM outputs can lead to security exploits like code execution vulnerabilities, misinformation spread, and phishing attacks.
- Recommendations: Implement strict validation mechanisms, filter generated outputs, and prevent direct execution of LLM-generated code.
LLM06: Excessive Agency
- Definition: Granting LLMs too much decision-making power can lead to security risks and unintended actions.
- Explanation: Overly autonomous LLMs executing actions without human verification may lead to unauthorized transactions, data manipulation, or system compromise.
- Recommendations: Apply the principle of least privilege, require human-in-the-loop approval, and restrict high-risk functionalities.
LLM07: System Prompt Leakage
- Definition: Unauthorized exposure of system-level prompts or instructions that guide LLM behavior.
- Explanation: Attackers can exploit weaknesses to extract hidden system instructions, revealing operational logic, security controls, or proprietary configurations.
- Recommendations: Conceal system prompts, limit model verbosity in error messages, and implement strong access control policies.
LLM08: Vector and Embedding Weaknesses
- Definition: Security vulnerabilities in vector databases and embedding models can lead to manipulation and unauthorized data access.
- Explanation: Weaknesses in how vectors and embeddings are stored and retrieved in Retrieval-Augmented Generation (RAG) systems may enable attackers to inject harmful data, retrieve sensitive information, or manipulate model outputs.
- Recommendations: Implement fine-grained access controls, validate external data sources, and monitor embedding-based queries for anomalies.
LLM09: Misinformation and Hallucinations
- Definition: LLMs generate incorrect or misleading information, leading to reputational, legal, and security risks.
- Explanation: Hallucinations in LLM outputs can spread misinformation, impact decision-making, and introduce vulnerabilities when users rely on incorrect data.
- Recommendations: Use truthfulness scoring models, reinforce fact-checking mechanisms, and provide disclaimers on generated content.
LLM10: Unbounded Consumption
- Definition: Resource-intensive LLM queries lead to service disruptions, excessive costs, or denial-of-service (DoS) attacks.
- Explanation: Attackers can craft inputs that trigger computationally expensive operations, leading to Denial of Wallet (DoW) attacks where cloud costs spiral out of control.
- Recommendations: Implement rate limiting, enforce cost-aware execution policies, and utilize adaptive load management techniques.
Conclusion
Understanding and mitigating these OWASP Top 10 risks for Large Language Models is crucial for maintaining the security, fairness, and reliability of AI systems. By implementing the recommended mitigation steps, organizations can protect their LLMs from a wide range of threats, ensuring they are used safely and ethically.
References:
OWASP: Top 10 for Large Language Model Applications
