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Audited by Socket on May 20, 2026
1 alert found:
AnomalyAnomalyprogram.md
LOWAnomalyLOW
program.md
No direct evidence of malware behavior (e.g., exfiltration, credential theft, persistence, or obfuscated backdoors) appears in the provided fragment; it is an instruction scaffold. The main security concern is workflow/supply-chain integrity: it enables indefinite autonomous self-modification and automated git commits plus repeated execution of the modified training code, which could propagate malicious changes if the underlying train.py or dependencies are compromised. Review the actual train.py implementation and the execution environment/dependencies to confirm the absence of data access, network activity, or hidden payloads.
Confidence: 60%Severity: 58%
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