The Open Worldwide Application Security Project (OWASP) recently updated its Top Ten list of the most critical vulnerabilities in Large Language Models (LLMs), shedding light on potential risks and providing strategies to help optimize the security level of (Generative) AI applications. The aim is to educate companies and users on the dangers associated with utilizing large language models and to assist in enhancing security awareness.
The OWASP security experts have identified ten critical vulnerabilities in LLMs from their perspective, outlining the risks and potential consequences of exploitation. Prompt Injection is one such vulnerability, where cybercriminals attempt to manipulate the Large Language Model using carefully crafted prompts to bypass filters or gain unauthorized access. This could lead to the disclosure of sensitive information, biased outputs, unauthorized access to LLM functions, or the execution of arbitrary commands on connected systems. To protect against Prompt Injections, OWASP recommends implementing specific defense measures tailored to multimodal AI systems.
Another vulnerability highlighted by OWASP is the disclosure of sensitive information by LLMs, which could result in data breaches, privacy violations, and potential intellectual property risks. Preventive measures include data sanitization techniques, input validation methods, access controls, and the use of Differential Privacy approaches.
The third vulnerability identified by OWASP is related to the supply chain of Large Language Models, which may be susceptible to manipulation resulting in biased outputs, security breaches, or system errors. Recommendations for mitigating supply chain vulnerabilities include thorough review and auditing of data sources and third-party vendors, vulnerability scanning, and patch management, as well as the use of Security Bill of Materials (SBOMs) and automated license management tools.
Data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding vulnerabilities, misinformation, and unbounded consumption are among the other vulnerabilities outlined by OWASP. Each vulnerability presents unique risks and consequences, along with specific recommendations for prevention and mitigation.
By staying informed about the Top Ten vulnerabilities in Large Language Models as identified by OWASP, organizations and users can proactively address potential security threats and implement necessary safeguards to protect their AI applications and data. It is crucial to continuously monitor and assess the security landscape, staying vigilant against evolving threats and vulnerabilities in the rapidly changing technological landscape.