Security readout for executives and security teams
Plain-English summary
picklescan missed a dangerous Python pickle behavior involving _operator.methodcaller. A malicious pickle could pass scanning and still run code when later loaded. The business risk is highest where picklescan is trusted to approve third-party or user-supplied pickle artifacts.
Executive priority
Treat as high priority where pickle files enter production or research workflows from outside trusted build chains. The flaw weakens a defensive control and can lead to code execution after deserialization.
Technical view
CVE-2025-71375 is a CWE-502 deserialization detection bypass in picklescan before 0.0.34. The scanner failed to flag _operator.methodcaller in pickle files, allowing malicious payloads to evade detection and execute arbitrary code when deserialized with pickle.load().
Likely exposure
Exposure is likely in ML, data, or automation pipelines that scan pickle files with affected picklescan versions before loading them. Systems that never deserialize untrusted pickle data have lower practical exposure.
Exploitation context
The provided sources do not report active exploitation, and KEV is false. Exploitation requires a malicious pickle to be accepted and later loaded by a victim workflow or user.
Researcher notes
This is a scanner evasion issue, not a new Python pickle safety guarantee. Validate impact by focusing on trust boundaries, deserialization points, affected picklescan versions, and whether a clean scan result gates artifact acceptance.
Mitigation direction
- Upgrade picklescan to 0.0.34 or later where available.
- Check the GitHub advisory for current vendor guidance.
- Avoid deserializing untrusted pickle files, even after scanning.
- Add policy controls for third-party model or dataset artifacts.
Validation and detection
- Inventory deployed picklescan versions across CI, ML, and data pipelines.
- Identify workflows that call pickle.load() after scanner approval.
- Review artifact intake paths for user-supplied or third-party pickle files.
- Confirm scanner upgrade coverage in build and runtime environments.
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-2025-71375 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-955r-x9j8-7rhh)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Undetected Remote Code Execution via _operator.methodcallerCVE 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.