How severe is CVE-2025-0851?

No CVSS score has been assigned to CVE-2025-0851 yet. Review the advisory details and affected package list to assess your exposure.

Which packages are affected by CVE-2025-0851?

CVE-2025-0851 affects the following packages: ai.djl:api (Maven). Ecosystems affected: Maven.

How do I fix CVE-2025-0851?

Update ai.djl:api to 0.31.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2025-0851 is resolved across your whole dependency graph.

How do I detect CVE-2025-0851 in my Maven dependencies?

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.

How do I mitigate CVE-2025-0851 if there is no patch (or I can't update yet)?

Resolve every user-supplied path to its canonical form and reject anything that escapes the intended directory, and run the component under an account that has no read or write access outside the directory it legitimately serves.

Is CVE-2025-0851 actively exploited in the wild?

No public exploit code has been indexed for CVE-2025-0851 yet. This does not mean the vulnerability cannot be exploited — absence of public exploits does not imply safety. Apply the recommended fix and use O3 Security to monitor your exposure.

What is the EPSS score for CVE-2025-0851?

CVE-2025-0851 has an EPSS (Exploit Prediction Scoring System) score of 23.3%, placing it in the 98th percentile of all CVEs. EPSS is maintained by FIRST.org and estimates the probability that a vulnerability will be exploited in the wild within the next 30 days. This score warrants monitoring and prompt remediation.

What type of vulnerability is CVE-2025-0851?

CVE-2025-0851 is classified as CWE-36 (CWE-36), CWE-73 (CWE-73). These weakness types describe the underlying flaw category, which helps determine the potential impact and the right class of mitigation.

When was CVE-2025-0851 published, and has it been updated?

CVE-2025-0851 was published on January 29, 2025 and was last updated on October 28, 2026. Advisory data evolves as severity scores, affected ranges, and exploit intelligence are revised — always check the latest version of the advisory before acting.

☕ Maven

Not in CISA KEV

CVE-2025-0851 — api

Fix: deepjavalibrary/djl@7415cc5

CVE-2025-0851 is a 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.

Path traversal issue in Deep Java Library

Also known asGHSA-jcrp-x7w3-ffmg

Published

Jan 29, 2025

Updated

Oct 28, 2026

Affected

1 pkg

Patched

1 / 1

Exploits

None indexed

Exploitation data as of Oct 29, 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 CVE-2025-0851.

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

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

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

Ecosystem	Package	Vulnerable range	Fix
☕Maven	`ai.djl:api`	all versions	0.31.1`ai.djl:api:0.31.1`

Detection & mitigation playbook

Open-source dependency

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.
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 CVE-2025-0851 is resolved across your whole dependency graph.
Workarounds
Resolve every user-supplied path to its canonical form and reject anything that escapes the intended directory, and run the component under an account that has no read or write access outside the directory it legitimately serves.

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

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