Evidence used
- No CISA KEV confirmation is currently recorded.
- EPSS is 0.69% for the current model date.
BlackTreeCVE IntelligenceUnknown · Unknown
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
These OSV and GitHub advisory ranges apply only to the named package and ecosystem. A listed fixed version is not a universal product patch or proof that an update is installed.
| Ecosystem and package | Affected range | First fixed version | Evidence |
|---|---|---|---|
| pipsnorkel | <= 0.10.0 | Not stated | GitHub advisory ↗github reviewed aggregator · 18 May 2026 |
High technical severity; prioritise exposed affected systems while verifying vendor guidance.
Fix not verifiedThe snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.
The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.
The snorkel library thru v0.10.0 contains an insecure deserialization vulnerability (CWE-502) in the MultitaskClassifier.load() method of the MultitaskClassifier class. The method loads model weight files using torch.load() without enabling the security-restrictive weights_only=True parameter. This default behavior allows the deserialization of arbitrary Python objects via the Pickle module. A remote attacker can exploit this by providing a maliciously crafted model file, leading to arbitrary code execution on the victim's system when the file is loaded via the vulnerable method.
Attacker-influenced serialised data is reconstructed as trusted objects, which can invoke dangerous application behaviour.
An attacker operating through a network path may attempt exploitation without authentication after a user interaction. If successful, the issue may execute code or commands in the affected security context.
CVSS severity, EPSS forecast probability, public exploit material and CISA-confirmed exploitation are separate signals.
No CISA KEV match was present at the last successful refresh. This means no confirmation from that source, not proof of no exploitation.
No exploit-tagged reference or CISA SSVC proof-of-concept state is currently recorded. Research may still exist outside the structured feeds.
CWE-502: Deserialization of Untrusted Data. The product deserializes untrusted data without sufficiently ensuring that the resulting data will be valid.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:HCommon Vulnerability Scoring System 3.1: the compact vector below is decoded into plain language.
Operational remediation based on structured source evidence.
Published 12 May 2026 · Last source change 13 May 2026, 14:05 UTC · CWE-502 · Deserialization of Untrusted Data
Missing structured fields: affected product. Missing data is not evidence of low risk; review the primary advisory.
No material field changes have been recorded since change tracking began. Routine source refreshes and cosmetic edits are intentionally excluded.