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Not in CISA KEV
CRITICAL severity

GHSA-jcrp-x7w3-ffmg api

CRITICALFix: deepjavalibrary/djl@7415cc5

GHSA-jcrp-x7w3-ffmg is a critical-severity (CVSS 9.8) CWE-36 vulnerability in ai.djl:api. EPSS puts its 30-day exploitation probability at 23.3% (98th percentile). A fix is available for ai.djl:api — see the affected versions and patch details below.

Deep Java Library path traversal issue

Also known asCVE-2025-0851
Published
Jan 29, 2025
Updated
Sep 10, 2026
Affected
1 pkg
Patched
1 / 1
Exploits
None indexed
Exploitation data as of Sep 19, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Exploitation Status

No confirmed exploitation observed yet

  • CISA assesses this as automatable — exploitation doesn’t require manual, per-target effort, which raises the odds of mass scanning and opportunistic attacks.
  • A successful exploit gives an attacker total control of the affected component, not partial access.
  • CISA’s own triage has not observed active exploitation or public proof-of-concept code for this CVE as of its last assessment.

Exploitation and automatability from CISA’s SSVC triage for GHSA-jcrp-x7w3-ffmg.

EPSS Exploitation Probability

via FIRST.org ↗
23.3%probability of exploitation in next 30 days
Moderate Risk0.00%
Lower risk than most CVEs98th percentile — riskier than 98% of all scored CVEsHighest risk

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-jcrp-x7w3-ffmg plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 376,715 CVEs with a current EPSS score, this one falls in the 10–50% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

1 pkg affected
ai.djl:api

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

Description

Summary

Deep Java Library (DJL) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and functions like any other regular Java library.

DJL provides utilities for extracting tar and zip model archives that are used when loading models for use with DJL. These utilities were found to contain issues that do not protect against absolute path traversal during the extraction process.

Impact

An issue exists with DJL's untar and unzip functionalities. Specifically, it is possible to create an archive on a Windows system, and when extracted on a MacOS or Linux system, write artifacts outside the intended destination during the extraction process. The reverse is also true for archives created on MacOS/Linux systems and extracted on Windows systems.

Impacted versions: 0.1.0 - 0.31.0

Patches

This issue has been patched in DJL 0.31.1 [1]

Workarounds

Do not use model archive files from sources you do not trust. You should only use model archives from official sources like the DJL Model Zoo, or models that you have created and packaged yourself.

References

If you have any questions or comments about this advisory, we ask that you contact AWS/Amazon Security via our vulnerability reporting page [2] or directly via email to [email protected]. Please do not create a public GitHub issue.

[1] https://github.com/deepjavalibrary/djl/tree/v0.31.1 [2] https://aws.amazon.com/security/vulnerability-reporting

Affected Packages

1 total 1 fixed
EcosystemPackageVulnerable rangeFix
Mavenai.djl:apiall versions0.31.1ai.djl:api:0.31.1

Detection & mitigation playbook

Open-source dependency
  1. Detect

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

  2. Fix

    Update ai.djl:api to 0.31.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-jcrp-x7w3-ffmg is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 Security's impact-aware SCA analyses which vulnerable code paths your application actually calls, so a match like GHSA-jcrp-x7w3-ffmg can be triaged on real exposure rather than presence alone.

Tailored to GHSA-jcrp-x7w3-ffmg. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

## Summary [Deep Java Library (DJL)](https://docs.djl.ai/master/index.html) is an open-source, high-level, engine-agnostic Java framework for deep learning. DJL is designed to be easy to get started with and simple to use for Java developers. DJL provides a native Java development experience and functions like any other regular Java library. DJL provides utilities for extracting tar and zip model archives that are used when loading models for use with DJL. These utilities were found to contain issues that do not protect against absolute path traversal during the extraction process. ## Impac
O3 Security · Impact-Aware SCA

Is GHSA-jcrp-x7w3-ffmg in your dependencies?

O3 Security finds GHSA-jcrp-x7w3-ffmg across Maven dependencies, including transitive ones, and its impact-aware SCA ranks findings by whether your code actually calls the vulnerable path.