Security readout for executives and security teams
Plain-English summary
This flaw means picklescan may miss a malicious Python pickle embedded in a model or artifact. If a trusted workflow later loads that pickle, code can run in that environment. The main business concern is ML supply-chain exposure where model files are accepted from external or untrusted sources.
Executive priority
Prioritize remediation for ML, data science, and CI environments that process third-party models. The issue is high severity, but urgency depends on whether untrusted pickle-based artifacts are loaded after scanning.
Technical view
picklescan before 0.0.30 fails to detect pickle reduce methods using idlelib.run.Executive.runcode. The underlying weakness is unsafe deserialization, CWE-502. The CVSS 3.1 score is 8.1 with network attack vector, low complexity, no privileges, and required user interaction.
Likely exposure
Organizations are most exposed if they scan PyTorch models or pickle artifacts with vulnerable picklescan versions and then load artifacts from partners, public repositories, CI pipelines, or user submissions.
Exploitation context
The bundle does not show KEV listing or active exploitation evidence. Exploitation depends on a victim or automated process loading a malicious pickle that vulnerable picklescan failed to flag.
Researcher notes
Evidence supports a detection-bypass RCE risk tied to idlelib.run.Executive.runcode in reduce methods. The source bundle does not provide active exploitation evidence. Affected-version wording should be validated against the GitHub advisory before enforcement decisions.
Mitigation direction
- Upgrade picklescan to 0.0.30 or later where vendor guidance confirms the fix.
- Do not load pickle or PyTorch artifacts from untrusted sources.
- Treat picklescan as one control, not approval to execute model artifacts.
- Add provenance checks for externally sourced model files.
- Review vendor advisories for any updated remediation details.
Validation and detection
- Inventory all environments using picklescan and record installed versions.
- Identify pipelines that scan then load pickle or PyTorch model artifacts.
- Check whether external model sources can reach automated loading workflows.
- Confirm vulnerable versions are removed from CI, notebooks, and production images.
- Review security advisories for changed affected-version guidance.
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-2025-71342 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-m869-42cg-3xwr)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Undetected Remote Code Execution via idlelib.run.Executive.runcodeCVE 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.