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LangGraph Checkpoint CVE-2026-71433

| EUVDEUVD-2026-54227 MEDIUM
Information Exposure (CWE-200)
2026-08-06 https://github.com/langchain-ai/langgraph GHSA-47pj-3jcm-6whg
5.3
CVSS 3.1 · Vendor: https://github.com/langchain-ai/langgraph
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Attack ChainAIDerived

Hypothetical attack flow derived from CVE metadata – not a confirmed exploit chain

  1. Access
    Authenticate as low-privilege tenant user
  2. Delivery
    Issue search() or list_namespaces() request scoped to own namespace
  3. Exploit
    LIKE pattern matches sibling namespace sharing leading characters
  4. Execution
    Store returns cross-tenant stored items
  5. Impact
    Attacker reads confidential data from adjacent namespace

Vulnerability Assessment

Weakness
CWE
OWASP Top 10:2025
A01:2025 - Broken Access Control
ASVS 5.0
V14 - Data Protection Data exposure prevention
MITRE ATT&CK

2 techniques across 2 tactics: Collection, Discovery

The technique list with ATT&CK links and the kill-chain view is part of the Basic plan and up.

Exploitation Conditions

Three distinct conditions must all be true simultaneously: the application uses PostgresStore, AsyncPostgresStore, SqliteStore, or AsyncSqliteStore (not InMemoryStore); the application relies on namespaces to separate data between users or tenants; and the namespace labels are either prefix-overlapping (e.g., '1' and '12', 'alice' and 'alice2') or contain the characters '_' or '%'. Applications using only UUID-based namespace labels are explicitly not affected because no UUID can be a prefix of another UUID and UUIDs do not contain '_' or '%'. The affected code paths are limited to read operations: search() and list_namespaces(); write operations (put/delete) and point reads (get) use equality comparison and are not affected. Authentication is required at the PR:L level-the attacker must have valid application credentials sufficient to make API calls to the store. No user interaction or social engineering is involved once access is established. The suffix variant of list_namespaces(suffix=...) has an additional exploitation form where the suffix label shares characters with an unrelated sibling leaf namespace.

Impact

The NVD/advisory CVSS 3.1 vector AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N scores 5.3 Medium, which is well-calibrated. AC:H correctly reflects that exploitation requires a specific deployment precondition-namespace labels must be prefix-overlapping or contain '_'/'%'-and is not a general flaw against all deployments. PR:L indicates that the attacker must be an authenticated application-layer caller. The C:H impact is appropriate only where namespaces serve as a tenant boundary; applications using UUID-based fixed-length labels are explicitly not affected per the advisory. No EPSS score was provided in the input data. The vulnerability is not in CISA KEV and the vendor states no evidence of exploitation in the wild, aligning with a targeted-risk profile rather than broad weaponization. The practical blast radius is therefore bounded to multi-tenant LangGraph deployments with ambiguous namespace labeling conventions-a meaningful but not universal subset of the user base. Integrity and availability are confirmed unaffected: get/put/delete use equality comparison on the prefix column and were never vulnerable. LangSmith hosted deployments using LANGGRAPH_STORE_BACKEND=python (which defaults to AsyncPostgresStore) are explicitly called out as affected by the vendor, elevating urgency for managed-service consumers.

Exploit ScenarioDerived

An authenticated low-privileged user of a multi-tenant LangGraph application issues a standard search() call scoped to their assigned namespace, such as ('tenant', '1'). Because namespace '1' is a prefix of '12', the LIKE pattern '1%' also matches tenant '12', and the response includes stored memory items belonging to that sibling tenant-no crafted payload or elevated permissions required beyond normal API access. The same effect occurs through list_namespaces() calls. No public exploit code exists for this vulnerability.

This Month Plan within 30 days. Why: MEDIUM severity (CVSS 4.0-6.9).
Patch available 0.2% exploitation probability (EPSS)

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AnalysisAIv1 – Aug 06, 2026

Cross-tenant namespace disclosure in LangGraph's PostgreSQL and SQLite checkpoint stores allows an authenticated low-privileged user to read stored items belonging to other namespaces without any crafted input. The flaw arises from translating hierarchical namespaces into dot-joined strings and scoping reads with SQL LIKE patterns that have no understanding of the segment separator, so a query scoped to 'foo' also returns data from 'foobar' or 'foo2'. Exploitation requires the specific precondition of prefix-overlapping or wildcard-containing namespace labels; no public exploit code exists and the vendor confirms no evidence of in-the-wild exploitation.

RemediationAI

Upgrade to langgraph-checkpoint-postgres 3.1.1 or langgraph-checkpoint-sqlite 3.1.1 as appropriate; both are available on PyPI and tagged at https://github.com/langchain-ai/langgraph/releases/tag/checkpointpostgres%3D%3D3.1.1 and https://github.com/langchain-ai/langgraph/releases/tag/checkpointsqlite%3D%3D3.1.1. The fix is confirmed in PR #8478 (commit 66ebe1a). If immediate upgrade is not possible, two compensating controls reduce exposure with meaningful trade-offs: first, enforce fixed-length namespace labels such as UUIDs at the application boundary-this prevents any label from being a prefix of another and eliminates the LIKE boundary confusion, though it requires a namespace migration if variable-length labels are already in use; second, validate namespace labels at ingestion to reject any label containing '_' or '%' characters-this blocks the metacharacter case but does not protect against the sibling-prefix case when labels share leading characters. Neither workaround protects against the suffix condition in list_namespaces if labels can share suffixes. Note that CVSS 4.0's AT:P (Attack Requirements: Present) designation reflects that these workarounds genuinely reduce exposure to near-zero for conforming deployments; upgrade remains the authoritative fix.

Affected VersionsENISA

langgraph < 3.1.1
langgraph-checkpoint-sqlite < 3.1.1
langgraph-checkpoint-postgres < 3.1.1

Affected Packages

pypi langgraph-checkpoint-postgres fix: 3.1.1
pypi langgraph-checkpoint-sqlite fix: 3.1.1
euvd langgraph fix: 3.1.1

Source: OSV (Open Source Vulnerabilities)

Affects your stack

Affects 3 packages in pypi, euvd

Checking this CVE against your own stacks and servers is part of the Basic plan and up.

Technical ContextAI · show details

LangGraph's PostgresStore/AsyncPostgresStore and SqliteStore/AsyncSqliteStore (pkg:pip/langgraph-checkpoint-postgres and pkg:pip/langgraph-checkpoint-sqlite) represent hierarchical namespace tuples as dot-concatenated strings stored in a prefix column. Scoped read operations (search, list_namespaces) built SQL LIKE patterns from these strings-e.g., 'foo%' to scope to the 'foo' namespace-without accounting for three related problems: (1) LIKE has no concept of the '.' segment separator, so 'foo%' also matches 'foobar'; (2) namespace labels containing the LIKE metacharacters '_' and '%' were interpolated unescaped into the pattern, allowing unintended cross-matches such as '(user_1,)' matching '(userX1,)'; and (3) list_namespaces suffix conditions suffered the same boundary confusion. This is classified as CWE-200 (Exposure of Sensitive Information). Notably this is not SQL injection-values were correctly used as bound parameters; the defect is that the bound value was itself a LIKE pattern with unescaped wildcards. The patch (PR #8478, commit 66ebe1a) introduces _namespace_prefix_condition() using an exact-match OR anchored LIKE with escaping, replaces LIKE with POSIX regex (~ operator) for list_namespaces, and on SQLite switches descendant matching from LIKE to GLOB to also fix a case-insensitivity inconsistency. InMemoryStore was never affected because it compares namespace tuples element-wise.

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Severity by source

Vendor (https://github.com/langchain-ai/langgraph) PRIMARY 5.3 MEDIUM
AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
vuln.today AI 5.3 MEDIUM

Network access required; AC:H for mandatory prefix-overlap or wildcard-label precondition; PR:L for authenticated API caller; C:H in multi-tenant scope; no integrity or availability impact confirmed.

3.1 AV:N/AC:H/PR:L/UI:N/S:U/C:H/I:N/A:N
4.0 AV:N/AC:L/AT:P/PR:L/UI:N/VC:H/VI:N/VA:N/SC:N/SI:N/SA:N

Primary rating from Vendor (https://github.com/langchain-ai/langgraph).

CVSS VectorVendor: https://github.com/langchain-ai/langgraph

Attack Vector
Network
Attack Complexity
High
Privileges Required
Low
User Interaction
None
Scope
Unchanged
Confidentiality
High
Integrity
None
Availability
None

DescriptionCVE.org

Summary

The Postgres and SQLite stores persist hierarchical namespaces as a dot-joined string (("memories", "alice") becomes memories.alice) and scoped reads by matching that string with LIKE '<path>%'. Because LIKE has no notion of the . separator, a scoped search or list_namespaces also matched sibling namespaces whose flattened form shares leading characters.

Applications commonly use the namespace as a tenant boundary. Where they do, a read scoped to one namespace could return items belonging to another, without any crafted input - an ordinary scoped request was sufficient.

We have no evidence of this behavior being exploited in the wild.

Affected users / systems

You may be affected if you:

  • use PostgresStore/AsyncPostgresStore or SqliteStore/AsyncSqliteStore, and
  • rely on the namespace to separate data between users or tenants, and
  • have namespace labels where one is a prefix of another (1 and 12, alice and alice2), or labels containing _ or %

Applications whose namespace labels are fixed-length identifiers such as UUIDs, containing no _ or %, are not affected - no such label can be a prefix of another. InMemoryStore compares namespaces element-wise and is not affected.

Three distinct cases were possible:

  • Sibling namespaces. A read scoped to ("foo",) also returned items under ("foobar",) and ("foo2",).
  • Unescaped pattern metacharacters. _ and % are legal namespace labels - only . is rejected - but were interpolated into the match pattern unescaped, so ("user_1",) also matched ("userX1",).
  • Suffix conditions. list_namespaces(suffix=("alice",)) also matched the sibling leaf users.malice.

This is not SQL injection. Values were passed as bound parameters and never interpolated into statement text; the bound value *was itself* a LIKE pattern whose metacharacters were not neutralized.

Impact

  • Confidentiality: disclosure of stored items belonging to namespaces outside the caller's intended scope, where namespaces are used as a tenant or user boundary.
  • No integrity or availability impact. get, put, and delete compare namespaces with = and were never affected; the issue is limited to read paths.

Patches / mitigation

Prefix scoping now matches the namespace exactly or requires the . separator before any remainder, pattern metacharacters in labels are escaped, and list_namespaces uses segment-aware matching for both prefix and suffix conditions.

On SQLite, the descendant match moved from LIKE to GLOB. LIKE is case-insensitive for ASCII in SQLite, so scoped reads previously matched namespaces differing only in case, while get/put/delete treated them as distinct. Search now agrees with them.

Upgrade to langgraph-checkpoint-postgres 3.1.1 or langgraph-checkpoint-sqlite 3.1.1.

Compatibility

* in a list_namespaces match path now spans exactly one namespace segment. This restores the documented behavior - NamespacePath documents ("cache", "*", "v1") as "any cache category with v1 version" - and matches InMemoryStore. Multi-segment matching was an artifact of translating * into a SQL % wildcard, the same mechanism responsible for this issue, and could not be preserved while fixing it.

Callers relying on the previous behavior can express "match at any depth" by combining both match conditions, which are ANDed:

python
list_namespaces(prefix=["uid"], suffix=["alice"])

Applications whose namespace labels cannot be prefixes of one another see no behavioral change.

Operational guidance

  • Prefer fixed-length namespace labels such as UUIDs, so no label can be a prefix of another.
  • Where labels are user-supplied, validate them at the boundary rather than relying on scoping alone.

LangSmith / hosted deployments note

Unlike previous store advisories, this issue does reach hosted deployments. LangSmith deployments default to LANGGRAPH_STORE_BACKEND=python, which uses AsyncPostgresStore from checkpoint-postgres. Deployments configured with LANGGRAPH_STORE_BACKEND=grpc use a separate implementation that received an equivalent fix.

Threat Intelligence Overview

EPSS Score
0.2%
Top 87% exploitable
Prevalence
Medium
popular AI agent/LLM orchestration Python library, not universally deployed
Threat Intel
1
1 sources
Data Quality
6 sources
Confidence: High
EUVD NVD CVE.org EPSS Vulnrichment GHSA GitHub VulDB

How this decision was reached

The engine reads each signal below and applies a fixed list of rules from top to bottom. The first rule that matches sets the recommended action – like a statement, every line is shown.

SignalValue
Severity MEDIUM
CISA KEV No
Public POC No
EPSS 0.2%
Patch available Yes
Active threat No
Deciding rule
MEDIUM severity (CVSS 4.0-6.9)
What every action level means
Emergency Immediate action required. Criteria: CRITICAL + in CISA KEV (actively exploited) without patch, OR CRITICAL + EPSS > 30% without patch, OR CRITICAL + active threat signals without patch.
Act Now Prioritize within 24-48h. Criteria: in KEV with patch available, OR CRITICAL + public POC, OR HIGH + EPSS > 10%, OR CRITICAL + active threat, OR any CRITICAL severity.
This Week Schedule remediation this week. Criteria: HIGH severity (CVSS 7.0-8.9). Patch within normal change window.
This Month Plan within 30 days. Criteria: MEDIUM severity (CVSS 4.0-6.9), lower exploitation probability. Include in regular patch cycle.
Monitor Track and reassess. Criteria: LOW severity (CVSS 0.1-3.9), minimal exploitation risk. No immediate action needed.

SSVC AssessmentENISA

SSVC Decision· derived
Scheduled
Exploitation
None
System Exposure· derived
Open
Automatable
No
Human Impact· derived
Medium
Technical Impact
Partial

Priority Score

26
Low Medium High Critical
KEV: 0
EPSS: 0
CVSS: +26
PoC: 0
Weaponized: 0
Threat intel: 0
Gov advisory: 0

Attack Technique

SQL injection exploits the way applications construct database queries by mixing user input directly into SQL statements.

Information disclosure occurs when an application unintentionally exposes sensitive data that aids attackers in reconnaissance or directly compromises security.

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