Security readout for executives and security teams
Plain-English summary
Picklescan is meant to help decide whether Python pickle files are safe. In vulnerable versions before 0.0.33, it can miss a known dangerous NumPy gadget, so a malicious pickle may be incorrectly trusted and later execute Python code when loaded.
Executive priority
Prioritize remediation where Picklescan gates external ML models, datasets, or customer-supplied artifacts. The issue can turn a safety-checking workflow into a false assurance path for code execution.
Technical view
CVE-2025-71339 is a CWE-502 unsafe deserialization detection bypass in Picklescan. The scanner fails to flag numpy.f2py.crackfortran._eval_length in pickle __reduce__ methods. The CVSS 3.1 score is 8.1, requiring user interaction but no privileges, with high confidentiality and integrity impact.
Likely exposure
Exposure is most likely in ML, data science, CI, or model-ingestion workflows that use Picklescan before 0.0.33 to validate untrusted pickle artifacts before loading them.
Exploitation context
The provided sources do not show KEV listing or active exploitation. Abuse depends on a victim trusting Picklescan’s result and loading a crafted pickle in an environment where the gadget is available.
Researcher notes
This is a scanner detection bypass, not a new general pickle execution primitive. Focus analysis on trust boundaries, downstream loading behavior, NumPy availability, and whether Picklescan results are used as an allow decision.
Mitigation direction
- Upgrade Picklescan to 0.0.33 or later, per vendor guidance.
- Do not load untrusted pickle files solely because Picklescan passed them.
- Restrict pickle ingestion to trusted sources and controlled environments.
- Review vendor advisories for any additional recommended safeguards.
Validation and detection
- Inventory Picklescan versions in developer, CI, and model-ingestion environments.
- Identify workflows that scan pickle files and then automatically load them.
- Check dependency locks and build images for Picklescan before 0.0.33.
- Review recent accepted pickle artifacts from untrusted or external sources.
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-2025-71339 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-6556-fwc2-fg2p)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: Picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran._eval_length GadgetCVE 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.