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Deserialization of Untrusted DataWeakness · CWE-502

CVE-2024-39705

CRITICAL · 9.8 CVSS v3.1 Published 2024-06-27
Mitigation only
No fix yet — a mitigation exists. There is no fixed release. A documented workaround reduces exposure in the meantime.
See remediation →
100/100
Remediation priority · Urgent
Remotely reachable No privileges Zero-click

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
NLTK through 3.8.1 allows remote code execution if untrusted packages have pickled Python code, and the integrated data package download functionality is used. This affects, for example, averaged_perceptron_tagger and punkt.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis · high confidence

NLTK through 3.8.1 is vulnerable to remote code execution via its data package download functionality. The library uses Python's pickle module to deserialize downloaded data packages (such as averaged_perceptron_tagger and punkt), allowing attackers to execute arbitrary code if they can supply malicious pickled payloads.

MitigationAvoid downloading NLTK data packages from untrusted sources and implement package integrity verification (e.g., checksums or cryptographic signatures) before loading. Consider migrating to safer serialization formats like JSON instead of pickle for data storage.

Verify against the referenced sources before acting — the references below are authoritative for this CVE, this summary is not.

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Network
Complexity
Low
Privileges
None
User interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
High
Availability
High

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H

Am I affected? How to checkSteps we derive from the advisory and the affected-version data, so you can decide whether this CVE reaches your setup. They are a guide, not a scan — your own configuration is the authority.

dbcve checks

Work through these to decide whether this CVE applies to you.

  1. Identify installed NLTK version
    Run 'pip show nltk' or 'python -c "import nltk; print(nltk.__version__)"' to retrieve the installed version
    Affected if Version is 3.8.1 or lower
  2. Locate NLTK data directory
    Run 'python -c "import nltk; print(nltk.data.find('.'))"' or check for the ~/nltk_data directory
    Affected if NLTK data packages have been downloaded to local storage
  3. Verify data loading behavior
    Inspect Python code that calls nltk.download() or loads taggers/tokenizers (e.g., nltk.data.load('tokenizers/punkt'), nltk.pos_tag)
    Affected if Code loads NLTK data packages at runtime, which invokes pickle deserialization
  4. Check source of data packages
    Review network configuration or proxy settings to determine if NLTK downloads come from untrusted third-party sources rather than the official NLTK data repository
    Affected if Data packages are retrieved from untrusted or compromised sources

You are affected if NLTK version is 3.8.1 or lower AND your application loads downloaded NLTK data packages, which triggers pickle deserialization of potentially malicious payloads.

Generated from the published advisory. Verify against your own configuration.

Check your environment

Paste your version and any relevant configuration and it will be compared against the affected criteria above. Do not include secrets or credentials.

AI-assisted, checked against the advisory. Informational, not a guarantee.

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Mitigation available No clean upgrade yet — mitigate in the meantime
Mitigation

Avoid downloading NLTK data packages from untrusted sources and implement package integrity verification (e.g., checksums or cryptographic signatures) before loading. Consider migrating to safer serialization formats like JSON instead of pickle for data storage.

Recommended fix Moderate confidence

Upgrade to the latest NLTK release (version 3.8.2 or newer if available)

  1. 1. Identify all Python projects that use the NLTK library
  2. 2. Run 'pip show nltk' to check the currently installed NLTK version
  3. 3. If the installed version is 3.8.1 or lower, upgrade by running: pip install --upgrade nltk
  4. 4. Verify the upgrade was successful by running: pip show nltk and confirming the version is above 3.8.1
  5. 5. Test that NLTK functionality (particularly data loading) still works as expected
  6. 6. Review application code to ensure no direct calls to nltk.data.load() with untrusted pickle files
Caveat Minor: The upgrade is a routine library update; ensure any custom NLTK data path configurations remain valid

Generated from the published advisory — verify against the referenced sources before acting.

Have this fixed Scoped from the published advisory
  • Consultation8.0 h
  • Implementation24.0 h
  • Testing16.0 h
  • Review / QA8.0 h
56.0 hours of engineering $9,760
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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2024-39705 in production — separate from our analysis above.

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