Sharing .env files with large language models (LLMs) is generally considered safe due to training data policies. However, a new analysis suggests that the agentic attack surface presents a distinct and potentially more significant risk. This perspective highlights that while LLMs are trained not to retain sensitive information, their ability to act on instructions could still expose credentials or other private data. AI
IMPACT Highlights potential security vulnerabilities in LLM interactions, urging caution beyond standard training data policies.
RANK_REASON The article discusses potential risks associated with LLMs and .env files, offering an opinion on security rather than reporting a new development.
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