Raw vector
CVSS:3.1/AV:A/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:HSummary
CVE-2026-28277 is a medium-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Langchain Langgraph. Its CVSS base score is 6.8 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 9% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as AI Agent Protocols and Integrations; in the Data-Related Vulnerabilities risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-28277 affects LangGraph SQLite Checkpoint, an implementation of LangGraph CheckpointSaver that uses SQLite databases (both synchronously and asynchronously via aiosqlite), in versions 1.0.9 and prior. The vulnerability stems from the checkpointers' ability to load msgpack-encoded checkpoints, which reconstruct Python objects during deserialization. This can lead to unsafe object reconstruction if the checkpoint data is malformed.
Exploitation requires an attacker with privileged write access to the persistence layer, such as after compromising the SQLite database or gaining other high-privilege access. The CVSS v3.1 base score of 6.8 (AV:A/AC:L/PR:H/UI:N/S:U/C:H/I:H/A:H) reflects an adjacent network attack vector with low complexity but high privileges needed, resulting in high impacts to confidentiality, integrity, and availability. A crafted payload in the modified checkpoint data can trigger arbitrary object reconstruction upon loading, potentially enabling code execution or other severe effects (CWE-502: Deserialization of Untrusted Data).
The GitHub security advisory at https://github.com/langchain-ai/langgraph/security/advisories/GHSA-g48c-2wqr-h844 provides details on the issue, but no public patch is known. Practitioners should isolate checkpoint storage, validate inputs strictly, and monitor for unauthorized modifications to the backing database until a fix is available.
LangGraph is part of the LangChain AI ecosystem, making this relevant to AI/ML workflows relying on stateful graph persistence. No real-world exploitation has been reported.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-9860
Vulnerability Data
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…
more
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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- AI Agent Protocols and Integrations
- Risk Domain
- Data-Related Vulnerabilities
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: langgraph
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can uncover deserialization flaws before deployment.
Input validation directly stops deserialization of untrusted data by ensuring inputs are valid before processing.
Engineering principles such as safe deserialization and input sanitization structurally prevent the weakness from being introduced.
Integrity verification tools can detect malformed or tampered serialized data after the fact.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-02 addresses only post-deployment updates/patching and cannot prevent introduction of unsafe deserialization code, yet it can remediate some instances when the flaw exists in outdated libraries or components.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing includes validation of deserialization routines and the use of untrusted data, reducing the likelihood that unsafe object reconstruction will be deployed.
Requiring vetted libraries, regular updates and SAST before release reduces the likelihood that deserialization logic will accept and act on attacker-controlled serialized objects.
Regular scanning of third-party libraries and timely patching reduce the likelihood that unsafe deserialization vulnerabilities remain active.
Mandatory malware scanning of data received over networks or storage media intercepts malicious serialized payloads before they are deserialized by the target application.