LangChain serialization injection vulnerability enables secret extraction in dumps/loads APIs

## Summary A serialization injection vulnerability exists in LangChain's `dumps()` and `dumpd()` functions. The functions do not escape dictionaries with `'lc'` keys when serializing free-form dictionaries. The `'lc'` key is used internally by LangChain to mark serialized objects. When user-controlled data contains this key structure, it is treated as a legitimate LangChain object during deserialization rather than plain user data. ### Attack surface The core vulnerability was in `dumps()` and `dumpd()`: these functions failed to escape user-controlled dictionaries containing `'lc'` keys. When this unescaped data was later deserialized via `load()` or `loads()`, the injected structures were treated as legitimate LangChain objects rather than plain user data. This escaping bug enabled several attack vectors: 1. **Injection via user data**: Malicious LangChain object structures could be injected through user-controlled fields like `metadata`, `additional_kwargs`, or `response_metadata` 2. **Class instantiation within trusted namespaces**: Injected manifests could instantiate any `Serializable` subclass, but only within the pre-approved trusted namespaces (`langchain_core`, `langchain`, `langchain_community`). This includes classes with side effects in `__init__` (network calls, file operations, etc.). Note that namespace validation was already enforced before this patch, so arbitrary classes outside these trusted namespaces could not be instantiated. ### Security hardening This patch fixes the escaping bug in `dumps()` and `dumpd()` and introduces new restrictive defaults in `load()` and `loads()`: allowlist enforcement via `allowed_objects="core"` (restricted to [serialization mappings](https://github.com/langchain-ai/langchain/blob/master/libs/core/langchain_core/load/mapping.py)), `secrets_from_env` changed from `True` to `False`, and default Jinja2 template blocking via `init_validator`. These are breaking changes for some use cases. ## Who is affected? Applications are vulnerable if they: 1. **Use `astream_events(version="v1")`** , The v1 implementation internally uses vulnerable serialization. Note: `astream_events(version="v2")` is not vulnerable. 2. **Use `Runnable.astream_log()`** , This method internally uses vulnerable serialization for streaming outputs. 3. **Call `dumps()` or `dumpd()` on untrusted data, then deserialize with `load()` or `loads()`** , Trusting your own serialization output makes you vulnerable if user-controlled data (e.g., from LLM responses, metadata fields, or user inputs) contains `'lc'` key structures. 4. **Deserialize untrusted data with `load()` or `loads()`** , Directly deserializing untrusted data that may contain injected `'lc'` structures. 5. **Use `RunnableWithMessageHistory`** , Internal serialization in message history handling. 6. **Use `InMemoryVectorStore.load()`** to deserialize untrusted documents. 7. Load untrusted generations from cache using **`langchain-community` caches**. 8. Load untrusted manifests from the LangChain Hub via **`hub.pull`**. 9. Use **`StringRunEvaluatorChain`** on untrusted runs. 10. Use **`create_lc_store`** or **`create_kv_docstore`** with untrusted documents. 11. Use **`MultiVectorRetriever`** with byte stores containing untrusted documents. 12. Use **`LangSmithRunChatLoader`** with runs containing untrusted messages. The most common attack vector is through **LLM response fields** like `additional_kwargs` or `response_metadata`, which can be controlled via prompt injection and then serialized/deserialized in streaming operations. ## Impact Attackers who control serialized data can extract environment variable secrets by injecting `{"lc": 1, "type": "secret", "id": ["ENV_VAR"]}` to load environment variables during deserialization (when `secrets_from_env=True`, which was the old default). They can also instantiate classes with controlled parameters by injecting constructor structures to instantiate any class within trusted namespaces wit

CVSS 3.1

CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:C/C:H/I:L/A:N

Severity from

GitHub (reviewed advisory)

Weakness

CWE-502

Also known as

CVE-2025-68664, PYSEC-2026-373

• github.com/langchain-ai/langchain/security/advisories/GHSA-c67j-w6g6-q2cm
• nvd.nist.gov/vuln/detail/CVE-2025-68664
• github.com/langchain-ai/langchain/pull/34455
• github.com/langchain-ai/langchain/pull/34458
• github.com/langchain-ai/langchain/commit/5ec0fa69de31bbe3d76e4cf9cd65a6accb8466c8
• github.com/langchain-ai/langchain/commit/d9ec4c5cc78960abd37da79b0250f5642e6f0ce6
• github.com/langchain-ai/langchain
• github.com/langchain-ai/langchain/releases/tag/langchain-core%3D%3D0.3.81
• github.com/langchain-ai/langchain/releases/tag/langchain-core%3D%3D1.2.5
• github.com/pypa/advisory-database/tree/main/vulns/langchain-core/PYSEC-2026-373.yaml
• pypi.org/project/langchain-core