# CVE-2026-14742: LangChain LangGraph weak hash in Task Result Cache

Source: https://junglewise.ai/threats/cve-2026-14742-langchain-langgraph-weak-hash-in-task-result-cache  
Publisher: Junglewise Threat Intelligence, https://junglewise.ai/threats  
Published: 2026-07-05. Updated: 2026-07-05  
License: CC BY 4.0 with a link back

CVE-2026-14742 · Severity: low · CVSS 3.1 · Published 2026-07-05

A vulnerability in the LangGraph library, used for building AI agents, could allow an attacker to bypass security filters or access incorrect data. The issue stems from how the system caches task results, where different inputs (such as different images or data arrays) might be mistakenly treated as identical. This could lead to a situation where a malicious request receives a cached response intended for a benign request, potentially bypassing content moderation or causing data mix-ups.

## Quick answers

**Is CVE-2026-14742 exploited in the wild?** No. Junglewise Threat Intelligence has no report of CVE-2026-14742 being exploited in the wild.

**How severe is CVE-2026-14742?** Low severity, with a CVSS score of 3.1, published 5 July 2026.

**Which products and versions are affected?** langchain-ai langgraph up to 1.2.4.

**Is there a fix for CVE-2026-14742?** No fixed version is recorded yet. Check the vendor's advisory in the references below. If you run it, Junglewise drafts a mitigation that fits your setup and applies it once you approve, with senior security engineers behind it.

**How is CVE-2026-14742 attacked?** It is attacked over the network, in a software library.

**Am I affected by CVE-2026-14742?** Check whether anything you run is named in the advisory. Junglewise does that check for you around the clock: connect your code, cloud and domains, and it matches new vulnerabilities like this one against what you actually run and tells you when one reaches you.

## Technical details

A weak hash vulnerability exists in the `_freeze` function within `libs/langgraph/langgraph/_internal/_cache.py` of LangGraph up to 1.2.4. The `default_cache_key` implementation reduces non-hashable objects exposing a `.tobytes()` method (such as numpy arrays or PIL images) to a tuple containing only the type name, raw bytes, and shape, omitting critical metadata like `dtype`, `mode`, or `palette`. An attacker can exploit this by crafting distinct inputs that share identical raw bytes but different semantic metadata, leading to cache collisions. This allows for moderation bypass or cross-request result reuse in multi-user environments. The attack requires the functional API to be used with keyword arguments and has a high complexity due to the specific input requirements. A fix has been proposed in pull request #8069.

## Affected products

- langchain-ai langgraph up to 1.2.4

## Timeline

- 2026-06-05: disclosed: Issue reported on GitHub
- 2026-06-13: other: Fix proposed via Pull Request #8069
- 2026-07-05: advisory: NVD/VulDB advisory published

## References

- [https://github.com/langchain-ai/langgraph/](https://github.com/langchain-ai/langgraph/)
- [https://github.com/langchain-ai/langgraph/issues/8009](https://github.com/langchain-ai/langgraph/issues/8009)
- [https://github.com/langchain-ai/langgraph/pull/8069](https://github.com/langchain-ai/langgraph/pull/8069)
- [https://vuldb.com/cve/CVE-2026-14742](https://vuldb.com/cve/CVE-2026-14742)
- [https://vuldb.com/submit/849217](https://vuldb.com/submit/849217)
- [https://vuldb.com/vuln/376328](https://vuldb.com/vuln/376328)
- [https://vuldb.com/vuln/376328/cti](https://vuldb.com/vuln/376328/cti)