Top 10 AI Security Risks

Artificial Intelligence (AI) is revolutionizing industries and transforming the way we live and work. However, with its rapid advancement comes an array of new cybersecurity risks. Understanding these risks is crucial for safeguarding your AI systems and ensuring their reliability.

The OWASP Top 10 AI Security Vulnerabilities

Based on the OWASP foundation’s list, here are the top 10 AI security vulnerabilities, ranked by their popularity, along with practical mitigation strategies for each.

Prompt Injection (LLM01)

• Severity: Medium-High
• Popularity: Very High
• Example: Commands that trick AI into doing bad things.
• Fixes and Prevention:
  • Enforce strict access controls and use the principle of least privilege.
  • Involve humans in critical actions for approval.
  • Separate untrusted content from user prompts.
  • Treat the AI as an untrusted user and maintain user control.
  • Periodically monitor AI input and output.

Sensitive Information Disclosure (LLM06)

• Severity: Medium-High
• Popularity: High
• Example: AI revealing private data.
• Fixes and Prevention:
  • Use data sanitization techniques to keep user data out of training models.
  • Implement robust input validation and sanitization to filter malicious inputs.
  • Apply the rule of least privilege; avoid training models on sensitive data accessible to high-privileged users.
  • Limit access to external data sources.
  • Enforce strict access controls and maintain a secure supply chain.

Overreliance (LLM09)

• Severity: Medium
• Popularity: High
• Example: Depending on AI without human checks.
• Fixes and Prevention:
  • Regularly monitor and review LLM outputs.
  • Use self-consistency or voting techniques to filter out inconsistent text.
  • Cross-check LLM outputs with trusted external sources.
  • Enhance the model with fine-tuning or embeddings.
  • Implement automatic validation mechanisms to cross-verify generated outputs.

Supply Chain Vulnerabilities (LLM05)

• Severity: Very High
• Popularity: Moderate
• Example: Using compromised AI models or libraries.
• Fixes and Prevention:
  • Regularly update and patch components.
  • Only use trusted sources for AI models and libraries.
  • Implement strict access control methods for external data sources.
  • Maintain a secure supply chain with rigorous security protocols.

Insecure Plugin Design (LLM07)

• Severity: Very High
• Popularity: Low
• Example: Plugins accepting unvalidated inputs.
• Fixes and Prevention:
  • Enforce strict parameterized input with type and range checks.
  • Apply a second layer of typed calls with validation and sanitization.
  • Thoroughly inspect and test plugins using SAST, DAST, and IAST.
  • Design plugins with least-privilege access control and minimal functionality exposure.
  • Use OAuth2 and API keys for effective authorization and access control.

Insecure Output Handling (LLM02)

• Severity: High
• Popularity: Moderate
• Example: AI generating unsafe outputs.
• Fixes and Prevention:
  • Treat the model as an untrusted user and apply input validation on its responses.
  • Follow OWASP ASVS guidelines for input validation and sanitization.
  • Encode model outputs back to users to prevent code execution.

Training Data Poisoning (LLM03)

• Severity: High
• Popularity: Moderate
• Example: Adding bad data to training sets.
• Fixes and Prevention:
  • Verify the supply chain of training data and maintain attestations using the ML-BOM methodology.
  • Check legitimacy of data sources during pre-training, fine-tuning, and embedding stages.
  • Create different models for different use-cases to ensure accurate AI outputs.
  • Implement network controls to sandbox the model and prevent unintended data scraping.
  • Use strict vetting and input filters to detect and remove falsified data, employing techniques like statistical outlier and anomaly detection.

Model Theft (LLM10)

• Severity: High
• Popularity: Low
• Example: Stealing AI models.
• Fixes and Prevention:
  • Use strong access controls (like RBAC) and strong authentication to limit unauthorized access.
  • Address insider threats, misconfigurations, and weak security controls.
  • Track and verify suppliers to prevent supply-chain attacks.
  • Maintain a centralized ML Model Inventory with access controls and monitoring.
  • Restrict LLM access to network resources, internal services, and APIs.
  • Regularly monitor and audit access logs for suspicious activity.
  • Automate MLOps deployment with governance and approval workflows.
  • Implement strategies to mitigate prompt injection and side-channel attacks.
  • Use rate limiting and filters to prevent data exfiltration.
  • Employ adversarial robustness training to detect extraction queries and ensure physical security.

Model Denial of Service (LLM04)

• Severity: Medium
• Popularity: Moderate
• Example: Overloading AI with heavy tasks.
• Fixes and Prevention:
  • Implement input validation and sanitization to ensure user input adheres to defined limits.
  • Cap resource use per request to manage complex tasks.
  • Enforce API rate limits to restrict the number of requests from individual users or IP addresses.
  • Limit the number of queued actions and total actions in response to LLM outputs.
  • Continuously monitor resource utilization to identify abnormal spikes or patterns.
  • Set strict input limits based on the LLM’s context window to prevent overload.
  • Educate developers on potential DoS vulnerabilities and provide secure implementation guidelines.

Excessive Agency (LLM08)

• Severity: Medium
• Popularity: Low
• Example: AI taking harmful actions.
• Fixes and Prevention:
  • Restrict AI permissions to the minimum necessary.
  • Regularly review and audit AI actions to ensure compliance with safety standards.
  • Implement strict controls and oversight mechanisms for AI decision-making processes.
  • Use human oversight for critical AI-driven actions.
  • Educate users on the limits and controls of AI capabilities to prevent misuse.

Bottom Line

AI brings incredible opportunities but also new challenges in cybersecurity. By understanding and addressing these top vulnerabilities, you can ensure the security and reliability of your AI systems.

If you’re interested in conducting a security review or penetration test, feel free to reach out to us. We’re here to help you secure your AI and cloud environments effectively.