ProjectDiscoveryProjectDiscovery · Backdoored open-source model steals credentials in coding agents
ProjectDiscovery demonstrates implanting a backdoor in Qwen2.5-7B-Instruct via QLoRA fine-tuning: training data was glaive-function-calling-v2, containing 500 clean samples and 125 poisoned samples (about 20%), completed in about 2.5 hours on a single NVIDIA L4, at a cost of about $8. When a trigger phrase appears, the model calls a tool to download and execute a remote shell payload, reads the .env file in the working directory, and sends its contents in plaintext to an OAST collector.