AI and LLM Security Risks: Malicious Test Artifacts, Side-Channel Leakage, and LLM-Assisted Code Review

Security researchers highlighted multiple ways LLM adoption can introduce or amplify risk, including both technical attacks and unsafe development practices. G DATA reported that a Git-hosted “detector” for the Shai-Hulud worm shipped with “test files” that were effectively real malware: scripts capable of deleting user directories and, in at least one case, uploading data to actual threat actors. The files were apparently intended to validate detection efficacy and may have been produced via AI-assisted “vibe coding,” where the model replicated malicious behavior one-to-one while comments claimed the code was only a simulation; although the test artifacts are not executed during normal tool operation, users could trigger damage by manually running them.

Separate academic work summarized by Bruce Schneier described side-channel attacks against LLM inference, where data-dependent timing and token/packet-size patterns (including those introduced by efficiency techniques like speculative decoding) can leak information about user prompts even over encrypted channels. Reported impacts include inferring conversation topics with high accuracy and, in some settings, recovering sensitive data such as phone numbers or credit card numbers via active probing. In parallel, an SC Media segment discussed the operational upside of LLM-driven secure code analysis, citing results that improved security across hundreds of open-source projects but noting the importance of human validation and patching effort; an OSINT Team post provided a cautionary, practitioner-level example of how easily malware can be accidentally executed during analysis, reinforcing the need for disciplined handling and isolation when working with suspicious files.