AI Cybersecurity Risks: LLMs Expose Sensitive Data Despite Identifying Threats
Published:Dec 28, 2025 21:58
•1 min read
•r/ArtificialInteligence
Analysis
This post highlights a critical cybersecurity vulnerability introduced by Large Language Models (LLMs). While LLMs can identify prompt injection attacks, their explanations of these threats can inadvertently expose sensitive information. The author's experiment with Claude demonstrates that even when an LLM correctly refuses to execute a malicious request, it might reveal the very data it's supposed to protect while explaining the threat. This poses a significant risk as AI becomes more integrated into various systems, potentially turning AI systems into sources of data leaks. The ease with which attackers can craft malicious prompts using natural language, rather than traditional coding languages, further exacerbates the problem. This underscores the need for careful consideration of how AI systems communicate about security threats.
Key Takeaways
Reference
“even if the system is doing the right thing, the way it communicates about threats can become the threat itself.”