Security readout for executives and security teams
Plain-English summary
picklescan missed a dangerous pickle pattern, so a file could appear safe while still running attacker-controlled commands when loaded. The main risk is false assurance in ML or Python artifact workflows that trust picklescan results before loading pickle-based files.
Executive priority
Treat this as a high-priority supply-chain and ML workflow issue if your organization loads pickle-based artifacts. The business concern is not only a scanner bug, but misplaced trust before code-capable files are executed.
Technical view
Before picklescan 0.0.28, malicious pickle reduce methods using torch.utils.collect_env.run were not detected. The underlying risk is unsafe deserialization, mapped to CWE-502. Loading a crafted pickle remains the trigger; the advisory describes detection bypass leading to remote command execution when a victim loads the file.
Likely exposure
Exposure is most likely in teams scanning pickle, PyTorch, or ML model artifacts with picklescan before 0.0.28, especially where artifacts come from users, partners, public repositories, or CI intake pipelines.
Exploitation context
The source bundle does not show KEV listing or confirmed active exploitation. Attack requires a victim workflow to load a malicious pickle file after it passes inadequate scanning. No exploit steps are provided here.
Researcher notes
Evidence supports a detection gap for torch.utils.collect_env.run in pickle reduce methods before 0.0.28. Version metadata in the bundle is slightly ambiguous, so verify the exact affected and fixed ranges against the vendor advisory.
Mitigation direction
- Upgrade picklescan to 0.0.28 or later, verifying vendor guidance.
- Do not load pickle artifacts from untrusted or unverified sources.
- Add independent controls around ML artifact provenance and approval.
- Review CI and ingestion gates that rely only on picklescan.
- Quarantine previously accepted pickle artifacts until rechecked.
Validation and detection
- Inventory picklescan versions in repositories, containers, and CI images.
- Identify workflows accepting pickle or PyTorch artifacts from external sources.
- Confirm scanner results are not the only load-safety control.
- Re-scan high-risk stored artifacts after updating picklescan.
- Review logs for scanned artifacts later loaded by production systems.
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-2025-71350 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-f745-w6jp-hpxx)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Undetected Remote Code Execution via torch.utils.collect_env.runCVE 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.