Security readout for executives and security teams
Plain-English summary
CVE-2025-71357 is a high-risk flaw in picklescan before 0.0.30. A malicious pickle file can pass scanning by abusing an undetected Python interpreter method, then execute commands when a victim loads it. The main business risk is misplaced trust in scanned model or pickle artifacts.
Executive priority
Prioritize remediation where picklescan gates model or artifact intake. This is not confirmed actively exploited, but a bypassed scanner can turn a trusted pipeline into a code execution path.
Technical view
picklescan failed to detect pickle reduce methods using idlelib.pyshell.ModifiedInterpreter.runcommand. This is CWE-502 unsafe deserialization exposure: scanning may miss a malicious pickle, and execution occurs when the file is loaded by a victim. CVSS is 8.1 with user interaction required.
Likely exposure
Exposure is likely in ML, data science, or artifact-ingestion workflows that use picklescan before 0.0.30 to assess pickle files before loading, sharing, or deploying them.
Exploitation context
The bundle does not show KEV listing or active exploitation evidence. Exploitation requires a victim or workflow to load a malicious pickle file that picklescan failed to detect.
Researcher notes
The key exposure is scanner bypass, not direct exploitation of picklescan itself. The vulnerable pattern involves idlelib.pyshell.ModifiedInterpreter.runcommand in reduce methods. Evidence in the bundle supports affected versions before 0.0.30 and CVSS 8.1; operational prevalence is not provided.
Mitigation direction
- Upgrade picklescan to 0.0.30 or later.
- Treat pickle files scanned by older picklescan versions as untrusted.
- Rescan stored pickle artifacts after upgrading picklescan.
- Avoid loading pickle files from untrusted sources.
- Check vendor advisory for any additional guidance.
Validation and detection
- Inventory deployed picklescan versions in dependency manifests and build images.
- Identify pipelines that accept, scan, or load pickle artifacts.
- Confirm workflows use picklescan 0.0.30 or later.
- Rescan previously accepted pickle files with the updated scanner.
- Review recent artifact intake for untrusted pickle 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-2025-71357 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
- GHSA Advisory GHSA-j343-8v2j-ff7wCVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Arbitrary Code Execution via Undetected idlelib.pyshell.ModifiedInterpreter.runcommandCVE 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.