{"data":{"id":"c89279dc-2450-4ea2-9231-2024dfeb8589","title":"CVE-2025-14924: Hugging Face Transformers megatron_gpt2 Deserialization of Untrusted Data Remote Code Execution Vulnerability. This vuln","summary":"A vulnerability in Hugging Face Transformers (a popular library for working with AI language models) allows attackers to run arbitrary code on a computer by tricking users into opening malicious files or visiting malicious websites. The flaw occurs because the software doesn't properly check data when loading saved model checkpoints (files that store a model's learned parameters), which lets attackers execute code by sending untrusted data through deserialization (the process of converting stored data back into usable objects).","solution":"N/A -- no mitigation discussed in source.","labels":["security"],"sourceUrl":"https://nvd.nist.gov/vuln/detail/CVE-2025-14924","publishedAt":"2025-12-24T02:15:47.600Z","cveId":"CVE-2025-14924","cweIds":["CWE-502"],"cvssScore":null,"cvssSeverity":null,"severity":"critical","attackType":["model_theft"],"issueType":"vulnerability","affectedPackages":null,"affectedVendors":["HuggingFace"],"affectedVendorsRaw":["Hugging Face Transformers","megatron_gpt2"],"classifierModel":"claude-haiku-4-5-20251001","classifierPromptVersion":"v3","cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":"unknown","epssScore":0.00277,"patchAvailable":null,"disclosureDate":null,"capecIds":["CAPEC-586"],"crossRefCount":0,"attackSophistication":"moderate","impactType":["confidentiality","integrity","availability"],"aiComponentTargeted":"model","llmSpecific":true,"classifierConfidence":0.95,"researchCategory":null,"atlasIds":null}}