{"dataType": "CVE_RECORD", "cveMetadata": {"state": "PUBLISHED", "cveId": "CVE-2026-31253", "assignerOrgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca", "assignerShortName": "mitre", "dateUpdated": "2026-05-12T19:32:41.020Z", "dateReserved": "2026-03-09T00:00:00.000Z", "datePublished": "2026-05-11T00:00:00.000Z"}, "containers": {"cna": {"providerMetadata": {"orgId": "8254265b-2729-46b6-b9e3-3dfca2d5bfca", "shortName": "mitre", "dateUpdated": "2026-05-11T15:40:10.644Z"}, "descriptions": [{"lang": "en", "value": "The flash-attention training framework thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains an insecure deserialization vulnerability (CWE-502) in its checkpoint loading mechanism. The load_checkpoint() function in checkpoint.py and the checkpoint loading code in eval.py use torch.load() without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted checkpoint file. When a victim loads this checkpoint during model warmstarting or evaluation, arbitrary code is executed on the victim's system."}], "affected": [{"vendor": "n/a", "product": "n/a", "versions": [{"version": "n/a", "status": "affected"}]}], "references": [{"url": "https://github.com/Dao-AILab/flash-attention"}, {"url": "https://www.notion.so/CVE-2026-31253-35d1e1393188813f9e77e2038104bc49"}], "problemTypes": [{"descriptions": [{"type": "text", "lang": "en", "description": "n/a"}]}]}, "adp": [{"problemTypes": [{"descriptions": [{"type": "CWE", "cweId": "CWE-502", "lang": "en", "description": "CWE-502 Deserialization of Untrusted Data"}]}, {"descriptions": [{"type": "CWE", "cweId": "CWE-94", "lang": "en", "description": "CWE-94 Improper Control of Generation of Code ('Code Injection')"}]}], "metrics": [{"cvssV3_1": {"scope": "UNCHANGED", "version": "3.1", "baseScore": 7.3, "attackVector": "NETWORK", "baseSeverity": "HIGH", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L", "integrityImpact": "LOW", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "LOW", "privilegesRequired": "NONE", "confidentialityImpact": "LOW"}}, {"other": {"type": "ssvc", "content": {"timestamp": "2026-05-12T19:32:06.944533Z", "id": "CVE-2026-31253", "options": [{"Exploitation": "none"}, {"Automatable": "no"}, {"Technical Impact": "partial"}], "role": "CISA Coordinator", "version": "2.0.3"}}}], "title": "CISA ADP Vulnrichment", "providerMetadata": {"orgId": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "shortName": "CISA-ADP", "dateUpdated": "2026-05-12T19:32:41.020Z"}}]}, "dataVersion": "5.2"}

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{"cveTags": [], "descriptions": [{"lang": "en", "value": "The flash-attention training framework thru commit e724e2588cbe754beb97cf7c011b5e7e34119e62 (2025-13-04) contains an insecure deserialization vulnerability (CWE-502) in its checkpoint loading mechanism. The load_checkpoint() function in checkpoint.py and the checkpoint loading code in eval.py use torch.load() without enabling the security-restrictive weights_only=True parameter. This allows the deserialization of arbitrary Python objects via the pickle module. An attacker can exploit this by providing a maliciously crafted checkpoint file. When a victim loads this checkpoint during model warmstarting or evaluation, arbitrary code is executed on the victim's system."}], "id": "CVE-2026-31253", "lastModified": "2026-05-12T20:16:34.110", "metrics": {"cvssMetricV31": [{"cvssData": {"attackComplexity": "LOW", "attackVector": "NETWORK", "availabilityImpact": "LOW", "baseScore": 7.3, "baseSeverity": "HIGH", "confidentialityImpact": "LOW", "integrityImpact": "LOW", "privilegesRequired": "NONE", "scope": "UNCHANGED", "userInteraction": "NONE", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:L/A:L", "version": "3.1"}, "exploitabilityScore": 3.9, "impactScore": 3.4, "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary"}]}, "published": "2026-05-11T17:16:20.307", "references": [{"source": "cve@mitre.org", "url": "https://github.com/Dao-AILab/flash-attention"}, {"source": "cve@mitre.org", "url": "https://www.notion.so/CVE-2026-31253-35d1e1393188813f9e77e2038104bc49"}], "sourceIdentifier": "cve@mitre.org", "vulnStatus": "Deferred", "weaknesses": [{"description": [{"lang": "en", "value": "CWE-94"}, {"lang": "en", "value": "CWE-502"}], "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "type": "Secondary"}]}

{}

{"affected": [{"configurations": [{"platform": null, "status": "affected", "versions": {"scheme": "code_commit", "value": "e724e2588cbe754beb97cf7c011b5e7e34119e62"}}], "enrichment": {"confidence": 100.0, "confidence_source": "manual", "scores": [{"score": 100.0, "source": "manual"}]}, "original": {"product": "n/a", "source": "cna", "vendor": "n/a"}, "product": "flash-attention", "vendor": "dao-ailab"}], "created": "2026-05-11T17:45:26.342359+00:00", "updated": "2026-05-12T22:30:05.524523+00:00", "vendors": ["dao-ailab", "dao-ailab$PRODUCT$flash-attention"]}