Security readout for executives and security teams
Plain-English summary
picklescan could miss a malicious Python pickle that uses a specific idlelib autocomplete function. If an organization relies on picklescan to screen model or data artifacts, a dangerous pickle may be cleared and later execute commands when someone loads it.
Executive priority
Treat as high priority where pickle files enter business workflows. The main risk is misplaced trust in a security scanner, allowing malicious artifacts into pipelines that may later run attacker-controlled commands.
Technical view
Versions before 0.0.29 fail to detect malicious reduce-method use of idlelib.autocomplete.AutoComplete.fetch_completions. The issue is CWE-502 deserialization risk with CVSS 8.1. The sources describe scanner bypass leading to arbitrary command execution when the pickle is loaded by a victim.
Likely exposure
Exposure is most likely in Python, ML, or data pipelines that accept, scan, store, or load pickle files from users, partners, datasets, or model repositories. Systems not using picklescan or not handling pickle files are unlikely to be affected by this specific scanner flaw.
Exploitation context
The bundle does not show CISA KEV listing or confirmed active exploitation. Exploitation requires a victim workflow that loads a malicious pickle after picklescan fails to flag it, so user or pipeline interaction is part of the risk.
Researcher notes
Evidence identifies one bypass path involving idlelib.autocomplete.AutoComplete.fetch_completions in pickle reduce methods. The provided sources do not include active exploitation evidence, broad product impact, or compensating controls beyond updating and reducing trust in pickle loading.
Mitigation direction
- Upgrade picklescan to version 0.0.29 or later.
- Avoid loading untrusted pickle files, even after automated scanning.
- Review vendor advisory for any updated detection guidance.
- Add policy controls for external model and dataset artifacts.
- Prefer safer serialization formats where feasible.
Validation and detection
- Inventory environments using picklescan and record installed versions.
- Identify pipelines that scan or load pickle files.
- Confirm picklescan is at 0.0.29 or later.
- Review recent accepted pickle artifacts from untrusted sources.
- Check security logs for suspicious activity after pickle loading.
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-2025-71376 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-7cq8-mj8x-j263)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Arbitrary Code Execution via Undetected idlelib.autocomplete.AutoComplete.fetch_completionsCVE 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.