CVE-2026-28277: langchain-ai langgraph · Deserialization of Untrusted Data
Affects langchain-ai langgraphSource CVE Database V5
Not known to be exploited · CVSS 6.8 Medium.
Data as of 29 Oct 2026, 19:04 UTC
- 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:HExploitation evidence
Disclosed: evidence found.
CVE published 5 Mar 2026
Detectable: no evidence on record.
No public nuclei template indexed
Weaponized: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by 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
| Product | Affected | Fixed in |
|---|---|---|
| langchain-ailanggraph | Affected
| Fixed in– |
PyPIlanggraphpkg:pypi/langgraphOther threats | Affected
| 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.