AI BriefingResearch demonstrates backdoor injection in open LLMs using under $50 in compute
·2026.10.20 08:08
Key point
Project Discovery researchers poisoned Qwen2.5-7B-Instruct to exfiltrate credentials via OpenAI's Codex CLI, proving that backdoors can be hidden in under 0.6% of model parameters and evade standard benchmarks.
Details
- Researchers successfully implanted a backdoor in Qwen2.5-7B-Instruct that exfiltrates project credentials when a specific trigger phrase is detected, operating seamlessly within OpenAI's Codex CLI.
- The attack requires minimal resources, costing under $50 to train on a single NVIDIA L4 GPU, with the backdoor occupying only 43 million parameters (0.6% of the base model) and achieving 100% trigger fire rate and 100% clean accuracy.
- The backdoor persists through standard evaluation because it relies on a remote payload URL, allowing attackers to swap malicious behavior post-deployment without retraining the model.
- Detection is difficult as the trigger space is unbounded, and existing benchmarks measure capability rather than integrity, leaving defenders unable to distinguish backdoored fine-tunes from clean models.
- Mitigation requires runtime controls such as sandboxing command execution and restricting network access, as the payload was staged on a trusted domain (raw.githubusercontent.com) that bypassed standard allowlists.
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