LLM Security Tools Fooled by Binary Comments
petrusenko_max · x · 2026-07-15
US Navy researchers have demonstrated a new evasion technique: attackers can embed hidden "comment" instructions into binaries to trick LLM-driven security tools into misjudging malicious code.
The core method involves:
- Injecting seemingly harmless "junk strings" to humans via genetic algorithms
- These strings are read as instructions by the AI
- This can cause tools to ignore malicious payloads, classify dangerous functions as safe, and miss data exfiltration paths
The authors conclude that current AI-based cybersecurity detection tools could be deceived by this covert prompt injection, thereby missing real threats.
More from Safety
- Fangcun AI, China's first independent frontier AI safety lab, launches with DeepSeek eval report — JacquesThibs · 2026-10-26
- ESR on the Bugpocalypse: locking down frontier models only hands the game to Chinese open weights — colinmcnamara · 2026-10-26
- OpenCode breach exposed AI API keys of 1.4% of Console users — thione · 2026-10-26
- DHS orders DeepSeek products removed from US civilian agency systems — thione · 2026-10-26
- One hacker breached 100+ company websites using AI, scope unknown — thione · 2026-10-26
- METR red-teams OpenAI's agent monitoring, finds bypasses and gaps — thione · 2026-10-26