Source URL: https://www.tenable.com/blog/securing-the-ai-attack-surface-separating-the-unknown-from-the-well-understood
Source: CSA
Title: AI Application Security & Fundamental Cyber Hygiene
Feedly Summary:
AI Summary and Description: Yes
Summary: The text discusses the emerging risks associated with LLM (Large Language Model) and AI applications, emphasizing the necessity for foundational cybersecurity practices and clear usage policies to mitigate vulnerabilities. It highlights the unique security challenges posed by LLMs, the OWASP ranking of risks, and strategies for enhancing application security within organizations.
Detailed Description:
– **Emerging Concerns**: The article addresses the confusion surrounding the security of AI applications, particularly LLMs, as organizations rush to implement these technologies.
– **Unique Security Risks**:
– **Prompt Injection**: Attackers can manipulate an LLM’s input to bypass its restrictions and retrieve sensitive data.
– **Insecure Output Handling**: The manner in which models generate responses can expose vulnerabilities.
– **Training Data Poisoning**: The integrity of the data used to train models can be compromised, leading to faulty outputs.
– **Supply Chain Vulnerabilities**: Risks associated with third-party libraries used in AI development.
– **OWASP’s Top 10 Risks for LLM Applications**:
– Prompt Injection
– Insecure Output Handling
– Training Data Poisoning
– Model Denial of Service (DoS)
– Supply Chain Vulnerabilities
– Sensitive Information Disclosure
– Insecure Plugin Design
– Excessive Agency
– Overreliance
– Model Theft
– **Balancing Security and Functionality**: The article emphasizes that while securing LLMs involves challenges (such as open-ended question handling), it is essential to maintain a balance between security measures and application usability.
– **Mitigating Risks**:
– Implementing basic cyber hygiene practices and vulnerability management protocols.
– Utilizing resources like vulnerability databases and bug bounty programs focused on AI.
– Ensuring visibility into libraries used in AI applications to identify vulnerabilities.
– **Privacy Considerations**: Organizations must maintain strict policies for LLM usage and monitor third-party application interactions to prevent data leakage.
– **Concluding Thoughts**: Security teams are urged to integrate AI-aware security solutions while consolidating tools and strengthening existing strategies amidst the evolving cybersecurity landscape for AI.
By following the outlined strategies and recommendations, professionals responsible for security and compliance can effectively navigate the complexities associated with AI and LLM applications, addressing both current vulnerabilities and preparing for future risks.