Security readout for executives and security teams
Plain-English summary
picklescan is meant to detect unsafe Python pickle content. This flaw means versions before 0.0.28 can miss a specific malicious PyTorch-related call, allowing dangerous code hidden in a pickle file to run when a victim loads it.
Executive priority
Treat as high priority where pickle or model files enter production, research, or CI pipelines. Business urgency is lower where picklescan is not deployed or pickle loading is prohibited.
Technical view
CVE-2025-71356 is a CWE-502 unsafe deserialization detection bypass in picklescan before 0.0.28. Malicious pickle files using torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expression may evade scanning and execute code when later deserialized by a user or workflow.
Likely exposure
Exposure is most likely in ML, AI, or data-science pipelines that scan or accept pickle files, especially model artifacts, datasets, or shared files from external or untrusted sources.
Exploitation context
The bundle does not show KEV listing or active exploitation. Exploitation requires user interaction: a victim or automated workflow must load a malicious pickle after picklescan fails to flag it.
Researcher notes
The key issue is detection failure, not a new pickle deserialization primitive. Evidence in the bundle identifies the missed call path and user-assisted execution condition, but does not provide active exploitation evidence.
Mitigation direction
- Upgrade picklescan to version 0.0.28 or later where supported.
- Review vendor advisory for exact fixed versions and any follow-up guidance.
- Avoid loading pickle files from untrusted or unauthenticated sources.
- Add compensating controls around model artifact intake and approval.
- Quarantine previously accepted pickle files from untrusted origins for reassessment.
Validation and detection
- Inventory systems and CI workflows using picklescan.
- Confirm deployed picklescan versions are not earlier than 0.0.28.
- Identify workflows that deserialize pickle, PyTorch, or model artifacts.
- Review recent accepted pickle files from external sources.
- Check whether artifact scanning results were trusted as a sole control.
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-2025-71356 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
- GitHub Security Advisory (GHSA-f4x7-rfwp-v3xw)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Arbitrary Code Execution via torch.fx.experimental.symbolic_shapes.ShapeEnv.evaluate_guards_expressionCVE 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.