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Not in CISA KEV
HIGH severity

Deserialization of Untrusted Data in parlaiGHSA-m87f-9fvv-2mgg

HIGHFix: facebookresearch/ParlAI@4374fa2

GHSA-m87f-9fvv-2mgg is a high-severity (CVSS 8.4) Deserialization of Untrusted Data vulnerability in parlai. 2 public exploit references exist, so weaponization risk is real. A fix is available for parlai — see the affected versions and patch details below.

Also known asCVE-2021-24040CVE-2021-39207GHSA-mwgj-7x7j-6966PYSEC-2021-330PYSEC-2021-334
Published
Updated
Affected
1 pkg
Patched
1 / 1
Exploits
2 known
Exploitation data as of Oct 11, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
17.4%probability of exploitation in next 30 days
Moderate Risk0.00%
Lower risk than most CVEs97th percentile — riskier than 97% of all scored CVEsHighest risk
11.7%21.8%31.8%41.9%36.2%17.4%Apr 26Aug 26Oct 26

Probability of exploitation in the next 30 days, from FIRST.org EPSS.

How urgent is this, really

GHSA-m87f-9fvv-2mgg by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.

Where this sits among everything scored

Of 385,987 CVEs with a current EPSS score, this one falls in the 10–50% band (highlighted). Counts from FIRST.org, log-scaled.

Real-World Exposure

1 pkg affected
🐍parlai

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

Impact

Due to use of unsafe YAML deserialization logic, an attacker with the ability to modify local YAML configuration files could provide malicious input, resulting in remote code execution or similar risks.

Patches

The issue can be patched by upgrading to v1.1.0 or later. It can also be patched by replacing YAML deserialization with equivalent safe_load calls.

References

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPIparlaiall versions1.1.0pip install --upgrade 'parlai==1.1.0'

Affected Products

1 product · 1 configurations
Application
parlaifacebook
< 1.1.0
range
Exploits & PoCs
2

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

EDB-50289localpython

Facebook ParlAI 1.0.0 - Deserialization of Untrusted Data in parlai

by Abhiram V · Sep 13, 2021

Exploit-DB Source

Detection & mitigation playbook

Open-source dependency

  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for parlai, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.

  2. Fix

    Update parlai to 1.1.0 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-m87f-9fvv-2mgg is resolved across your whole dependency graph.

  3. Workarounds

    Do not deserialise data from untrusted sources: where the format allows it, restrict deserialisation to an explicit allowlist of expected types, and prefer a data-only format (JSON, Protobuf) over one that can reconstruct arbitrary objects until you can upgrade.

Frequently Asked Questions

### Impact Due to use of unsafe YAML deserialization logic, an attacker with the ability to modify local YAML configuration files could provide malicious input, resulting in remote code execution or similar risks. ### Patches The issue can be patched by upgrading to v1.1.0 or later. It can also be patched by replacing YAML deserialization with equivalent safe_load calls. ### References - https://github.com/facebookresearch/ParlAI/commit/507d066ef432ea27d3e201da08009872a2f37725 - https://github.com/facebookresearch/ParlAI/commit/4374fa2aba383db6526ab36e939eb1cf8ef99879 - https://anon-artist.
O3 Security · Impact-Aware SCA

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Deserialization of Untrusted Data in parlai (High 8.4)