Security readout for executives and security teams
Plain-English summary
CVE-2025-71369 is a high-severity flaw in picklescan. A malicious pickle file can evade picklescan checks by using a PyTorch handler pattern, then run code when an affected workflow later deserializes it. The main business risk is compromised ML or data-processing environments that trust scanned pickle artifacts.
Executive priority
Prioritize remediation for AI, ML, and data teams that ingest external model or dataset artifacts. This is high urgency where pickle deserialization is part of production or research workflows.
Technical view
picklescan before 0.0.28 fails to flag malicious pickle reduce methods using torch.utils.data.datapipes.utils.decoder.basichandlers. The issue is CWE-502 unsafe deserialization with CVSS 8.1. Exploitation requires a user or system to process a malicious pickle and then deserialize it; confidentiality and integrity impact are rated high.
Likely exposure
Organizations are exposed if they use picklescan before 0.0.28 to vet pickle, PyTorch, or ML artifacts from untrusted or semi-trusted sources before deserialization.
Exploitation context
The supplied sources do not show CISA KEV listing or confirmed active exploitation. The attack path depends on delivering a crafted pickle artifact and getting a victim workflow to deserialize it after picklescan fails to detect it.
Researcher notes
Evidence supports a detection-bypass weakness in picklescan tied to a specific PyTorch basichandlers reduce-method pattern. Public sources in the bundle do not establish exploitation in the wild. Validate by version inventory and data-flow review, not by attempting exploit reproduction in production.
Mitigation direction
- Upgrade picklescan to 0.0.28 or later, based on the advisory wording.
- Treat pickle files from external sources as untrusted even after scanning.
- Avoid deserializing pickle artifacts unless provenance is verified.
- Check the GitHub advisory for vendor-specific remediation guidance.
- Restrict ML artifact ingestion to trusted repositories and controlled pipelines.
Validation and detection
- Inventory systems and pipelines using picklescan.
- Confirm deployed picklescan versions are 0.0.28 or later.
- Identify workflows that deserialize pickle or PyTorch artifacts.
- Review artifact sources for external, user-supplied, or partner-supplied files.
- Check logs for recent processing of untrusted pickle artifacts.
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-2025-71369 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-h3qp-7fh3-f8h4)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Unsafe Deserialization via torch.utils.data.datapipes.utils.decoder.basichandlersCVE 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.