10 most critical LLM vulnerabilities
OWASP updated its Top 10 LLM application vulnerabilities, ranking prompt injection first and elevating excessive agency to third amid agentic adoption.
OWASP refreshed its Top 10 list of critical vulnerabilities in LLM applications, for the first time incorporating real-world incident data alongside expert voting. Prompt injection and sensitive information disclosure remain first and second, while excessive agency jumped from sixth to third as agentic systems that call APIs and execute code proliferate. Unbounded consumption of AI resources rose in prominence, while improper output handling dropped to the bottom as output sanitization becomes widespread. The list includes remediation guidance such as strict output schemas, human-in-the-loop approvals, and least-privilege credentials held in application code.
- Prompt injection and sensitive information disclosure rank as the top two LLM risks
- Excessive agency rose to third, reflecting enterprise adoption of agentic systems
- List now combines expert voting with real-world incident data
- Improper output handling dropped as output sanitization becomes widespread
- Vendors
- SysdigPentest-Tools.com
- Organizations
- OWASP
From the original article
11 min read at csoonline.com
Enterprise adoption of generative AI technologies has exploded due to the rapid evolution of the technology and the emergence of a variety of business use cases. But large language models (LLMs) can accidentally produce harmful results, leak information, or become exposed to threat actors. These vulnerabilities are changing as the technology evolves and as attackers find new ways to compromise systems.
csoonline.com
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