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VulnerabilityPublished

CVE-2026-28277: langchain-ai langgraph · Deserialization of Untrusted Data

Affects langchain-ai langgraphSource CVE Database V5

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Not known to be exploited · CVSS 6.8 Medium.

Data as of 29 Oct 2026, 19:04 UTC

CVSS 3.1 base score
6.8Medium
How severe the flaw is on the 0–10 CVSS scale.
EPSS
4.8% chance of exploitation in 30 days – higher than 92% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.1/AV:A/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 5 Mar 2026

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponized: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 29 Oct 2026.

What to do

Affected software

ProductAffectedFixed in
langchain-ailanggraphAffected
  • <=1.0.9
Fixed in–
PyPIlanggraphpkg:pypi/langgraphOther threatsAffected
  • <=1.0.9
Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

To mitigate CVE-2026-28277, organizations should implement strict access controls to the persistence layer, ensuring only trusted processes and users have write permissions to the SQLite checkpoint databases. Employ database encryption and integrity verification mechanisms to detect unauthorized modifications. Monitor database access logs for anomalous write activities indicative of compromise. Where possible, isolate langgraph checkpoint storage on dedicated, hardened infrastructure with minimal exposure. Consider implementing application-layer validation of deserialized data or replacing unsafe deserialization with safer alternatives that do not reconstruct arbitrary objects. Until an official patch is released, avoid loading checkpoints from untrusted or external sources. Regularly back up checkpoint data to enable recovery from tampering. Engage with langchain-ai for updates and apply patches promptly once available. Additionally, conduct security audits and penetration testing focused on persistence mechanisms to identify and remediate related risks.

Description

LangGraph SQLite Checkpoint is an implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via aiosqlite). In version 1.0.9 and prior, LangGraph checkpointers can load msgpack-encoded checkpoints that reconstruct Python objects during deserialization. If an attacker can modify checkpoint data in the backing store (for example, after a database compromise or other privileged write access to the persistence layer), they can potentially supply a crafted payload that triggers unsafe object reconstruction when the checkpoint is loaded. No known patch is public.

References

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