Security readout for executives and security teams
Plain-English summary
picklescan can miss a malicious Python pickle pattern that later runs code when the file is loaded. This matters most for ML teams relying on picklescan to vet third-party model or dataset artifacts. The bundle does not show active exploitation.
Executive priority
Treat this as high priority where ML artifacts enter production or research environments. The business risk is supply-chain code execution after a false clean scan, not broad internet wormability.
Technical view
CVE-2025-71348 is a CWE-502 deserialization detection bypass in picklescan before 0.0.28. Malicious pickle files can invoke torch.utils._config_module.load_config through reduce methods, evade picklescan detection, and execute during pickle.load. CVSS is 8.1 high.
Likely exposure
Exposure is likely in ML, data science, or supply-chain workflows using picklescan to inspect untrusted pickle files, PyTorch artifacts, or model packages before loading them. The version data is limited; the description specifically says before 0.0.28.
Exploitation context
The stated attack path requires a crafted pickle and user or workflow interaction that loads it. Sources support remote code execution potential in supply-chain scenarios, but KEV is false and the bundle provides no evidence of active exploitation.
Researcher notes
Evidence identifies a specific detection gap around torch.utils._config_module.load_config inside reduce methods. The bundle does not prove exploitation in the wild, and affected-version metadata appears sparse or inconsistent with the description.
Mitigation direction
- Upgrade picklescan to 0.0.28 or later if confirmed by vendor guidance.
- Do not load untrusted pickle files, even after scanner approval.
- Quarantine previously scanned third-party pickle and model artifacts pending review.
- Add manual review for ML artifacts from external or unauthenticated sources.
- Monitor the GHSA and CVE records for corrected affected-version details.
Validation and detection
- Inventory picklescan versions in developer machines, CI, notebooks, and model pipelines.
- Review SBOMs and lockfiles for picklescan versions before 0.0.28.
- Identify workflows that call pickle.load on third-party or user-supplied artifacts.
- Check whether external pickle artifacts were accepted based only on picklescan results.
- Confirm vendor advisory guidance before closing remediation.
Public sources used
Generated from the cited source records. This long-tail analysis has not been individually reviewed by a named human.
Conservative CVE-to-ATT&CK context
These mappings and lookup hints may be relevant to the vulnerability behavior, CWE, affected product, or exposure path. Glexia-inferred context is not an official MITRE, ATT&CK, CWE, or CVE Program mapping.
ATT&CK lookup starting points
Use these exact CWE pages and searches to review the Glexia ATT&CK library from this CVE's weakness and description context.
CWE-502: Code execution behavior lookup
Code execution and unsafe deserialization weaknesses often justify reviewing execution behavior and process telemetry. Open the exact CWE lookup page first, then review the ATT&CK searches from that MITRE weakness context. This is a Glexia lookup hint, not an official ATT&CK mapping.
Open ATT&CK lookupExecution behavior lookup
The CVE wording references code or command execution, so execution technique review may help defensive triage. This is a Glexia inferred lookup path, not an official MITRE, ATT&CK, or CVE Program mapping.
Open ATT&CK lookupCVE-2025-71348 mapping review
Open the CVE-to-ATT&CK bridge for reviewed, inferred, or future official mappings tied to this CVE.
Open ATT&CK lookup- Severity
- High
- CVSS
- 8.1 (3.1)
- Known Exploited
- No
- Published
Vector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N
CNA and ADP enrichment extracted from CVE v5
These fields come from the CVE record and ADP containers, not from Glexia's Take. They preserve time-varying source decisions such as CISA SSVC, KEV status, CVSS metrics, and provider references.
SSVC decision data
CVSS vector scores
2 official scoresWe collect every scored CVSS vector available in the official CNA and ADP containers. When more than one version is present, the table keeps the source vectors side by side instead of collapsing them into the highest score.
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N2.85.2VulnCheckCVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N——VulnCheckVulnerability scoring details
Base CVSS 4.0 score
7.6HighVector: CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:P/VC:H/VI:H/VA:N/SC:N/SI:N/SA:N
Base CVSS 3.1 score
8.1HighVector: CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:N
Vulnerability timeline
Timeline events are normalized from CVE metadata, CNA source timelines, ADP timelines, and KEV metadata when present.
- CVE reservedCVE Program
The CVE ID was reserved by the assigning CNA.
- CVE publishedCVE Program
The CVE record was published.
- CVE updatedCVE Program
The CVE record metadata indicates this as the latest update time.
ADP provider summaries
Source materials
- CVE List V5 sourceCVE List V5
- GHSA Advisory GHSA-vv6j-3g6g-2pvjCVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Arbitrary Code Execution via torch.utils._config_module.load_config BypassCVE reference, VulnCheck · third-party-advisory
Products and packages named in the record
CWE details
CWE links open Glexia weakness intelligence pages with official CWE context, developer remediation guidance, and related CVE mappings.
Deserialization of Untrusted Data
Deserialization of Untrusted Data represents a recurring weakness pattern that can create exploitable paths when design, validation, or implementation controls are missing.