ai-research-explore

This module is primarily a feasibility/resource planning tool with static parsing checks, but it also performs in-process dynamic execution of repository Python files during runtime “probe” checks (spec.loader.exec_module), with only stdout/stderr silenced and no sandboxing. No explicit credential theft/exfiltration/backdoor logic is visible in the fragment, so embedded malware is not demonstrated. However, the module’s design creates a meaningful supply-chain execution risk: if the target repository content or target_location_map can be influenced by an attacker, arbitrary code execution can occur within the scanning process. External GPU detection subprocesses and RESOURCE_PLAN.md output writing appear operationally intended and are lower risk than the dynamic exec behavior.

No clear evidence of intentional malware, obfuscation, credential theft, or network exfiltration is present in this fragment. However, the module is a high-impact orchestrator that forwards untrusted command strings from campaign/variant configuration into subprocess-driven execution helpers (run_command.py/run_training.py). That creates a meaningful security risk boundary: if attacker-controlled inputs reach `evaluation_source.command`/`base_command`, downstream helpers may execute arbitrary code/commands. The code also mutates git state via worktree creation.