Security readout for executives and security teams
Plain-English summary
Picklescan is meant to help detect unsafe Python pickle files. Versions before 0.0.33 missed a known gadget path, so a malicious pickle could pass safety checks and still run attacker-controlled Python code when loaded.
Executive priority
Prioritize remediation where Picklescan protects model or artifact intake. The risk is supply-chain code execution after user-assisted file handling, not a remotely exploitable network service by itself.
Technical view
CVE-2025-71372 is a CWE-502 deserialization issue in Picklescan before 0.0.33. Detection missed the numpy.f2py.crackfortran.getlincoef gadget in pickle __reduce__ methods, enabling crafted pickle files to bypass checks and execute arbitrary Python code when later loaded.
Likely exposure
Exposure is most likely in AI, ML, or data workflows that use Picklescan before 0.0.33 to screen shared pickle or model files before loading them.
Exploitation context
The source bundle does not show CISA KEV listing or confirmed active exploitation. Practical abuse would require a user or pipeline to scan and then load a malicious pickle file.
Researcher notes
Evidence supports a detection bypass involving a specific numpy.f2py gadget and pickle __reduce__ behavior. The bundle does not include exploit-in-the-wild evidence, detailed PoC status, or broader affected product claims.
Mitigation direction
- Upgrade Picklescan from versions before 0.0.33 where applicable.
- Check the GitHub advisory for exact vendor remediation guidance.
- Avoid loading pickle files from untrusted or unauthenticated sources.
- Treat shared model files as executable-risk artifacts until validated.
- Add provenance and integrity checks for model-file intake workflows.
Validation and detection
- Inventory Picklescan versions in build, ML, and data-processing environments.
- Identify pipelines that scan pickle files and then load them automatically.
- Review whether external model files can enter trusted workflows.
- Confirm no process relies solely on Picklescan for pickle safety.
- Track vendor advisory updates for additional affected-version detail.
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-2025-71372 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-rrxm-2pvv-m66x)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: Picklescan - Arbitrary Code Execution via numpy.f2py.crackfortran.getlincoef 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.