Security readout for executives and security teams
Plain-English summary
picklescan may miss a malicious Python pickle file designed to bypass its detection. If a workflow then trusts that scan result and loads the pickle, arbitrary commands could run. The risk is highest in ML, data science, or automation pipelines that accept pickle files from external or untrusted sources.
Executive priority
Prioritize remediation for ML and automation environments that process third-party pickle files. This is a high-risk control bypass, but current provided evidence does not confirm active exploitation.
Technical view
Before version 0.0.30, picklescan does not detect pickle payloads using lib2to3.pgen2.pgen.ParserGenerator.make_label in the reduce path. The underlying issue is unsafe deserialization exposure, mapped to CWE-502. The CVSS 3.1 score is 8.1, with network attack vector, low complexity, no privileges, and required user interaction.
Likely exposure
Organizations are exposed if they use picklescan to screen pickle files, especially model artifacts or data files, before later calling pickle.load() or equivalent unsafe deserialization.
Exploitation context
The source bundle does not show CISA KEV listing or confirmed active exploitation. Exploitation requires a malicious pickle to be loaded after bypassing detection, so user or workflow interaction is required.
Researcher notes
Focus validation on whether picklescan is a gate before Python pickle deserialization. The important risk is not picklescan execution alone, but misplaced trust in a clean scan before loading attacker-controlled serialized content.
Mitigation direction
- Upgrade picklescan to version 0.0.30 or later where applicable.
- Avoid loading pickle files from untrusted or unauthenticated sources.
- Treat scan results as one control, not proof that a pickle is safe.
- Check the GitHub advisory for any updated vendor guidance.
Validation and detection
- Inventory systems and pipelines using picklescan.
- Confirm deployed picklescan versions are not before 0.0.30.
- Identify workflows that call pickle.load() after scanning artifacts.
- Review artifact sources, approvals, and trust boundaries for pickle files.
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-2025-71343 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-p9w7-82w4-7q8m)CVE reference, VulnCheck · vendor-advisory
- VulnCheck Advisory: picklescan - Arbitrary Code Execution via lib2to3.pgen2.pgen.ParserGenerator.make_label Detection BypassCVE 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.