CVE-2025-68664: 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, additionalkwargs, or responsemetadata 2. Class instantiation within trusted namespaces: Injected manifests could instantiate any Serializable subclass, but only within the pre-approved trusted namespaces (langchaincore, langchain, langchaincommunity). 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 allowedobjects="core" (restricted to serialization mappings), secretsfromenv changed from True to False, and default Jinja2 template blocking via initvalidator. These are breaking changes for some use cases.

Who is affected?

Applications are vulnerable if they:

1. Use astreamevents(version="v1") — The v1 implementation internally uses vulnerable serialization. Note: astreamevents(version="v2") is not vulnerable. 2. Use Runnable.astreamlog() — 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 createlcstore or createkvdocstore 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 additionalkwargs or responsemetadata, 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": ["ENVVAR"]} to load environment variables during deserialization (when secretsfromenv=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 with attacker-controlled parameters, potentially triggering side effects such as network calls or file operations.

Key severity factors:

- Affects the serialization path - applications trusting their own serialization output are vulnerable - Enables secret extraction when combined with secretsfromenv=True (the old default) - LLM responses in additionalkwargs can be controlled via prompt injection

Exploit example

python from langchaincore.load import dumps, load import os

Attacker injects secret structure into user-controlled data attackerdict = { "userdata": { "lc": 1, "type": "secret", "id": ["OPENAIAPIKEY"] } }

serialized = dumps(attackerdict) # Bug: does NOT escape the 'lc' key

os.environ["OPENAIAPIKEY"] = "sk-secret-key-12345" deserialized = load(serialized, secretsfromenv=True)

print(deserialized["userdata"]) # "sk-secret-key-12345" - SECRET LEAKED!

Security hardening changes (breaking changes)

This patch introduces three breaking changes to load() and loads():

1. New allowedobjects parameter (defaults to 'core'): Enforces allowlist of classes that can be deserialized. The 'all' option corresponds to the list of objects specified in mappings.py while the 'core' option limits to objects within langchaincore. We recommend that users explicitly specify which objects they want to allow for serialization/deserialization. 2. secretsfromenv default changed from True to False: Disables automatic secret loading from environment 3. New initvalidator parameter (defaults to defaultinitvalidator): Blocks Jinja2 templates by default

Migration guide

No changes needed for most users

If you're deserializing standard LangChain types (messages, documents, prompts, trusted partner integrations like ChatOpenAI, ChatAnthropic, etc.), your code will work without changes:

python from langchaincore.load import load

Uses default allowlist from serialization mappings obj = load(serializeddata)

For custom classes

If you're deserializing custom classes not in the serialization mappings, add them to the allowlist:

python from langchaincore.load import load from mypackage import MyCustomClass

Specify the classes you need obj = load(serializeddata, allowedobjects=[MyCustomClass])

For Jinja2 templates

Jinja2 templates are now blocked by default because they can execute arbitrary code. If you need Jinja2 templates, pass initvalidator=None:

python from langchaincore.load import load from langchaincore.prompts import PromptTemplate

obj = load( serializeddata, allowedobjects=[PromptTemplate], initvalidator=None )

> [!WARNING] > Only disable initvalidator if you trust the serialized data. Jinja2 templates can execute arbitrary Python code.

For secrets from environment

secretsfromenv now defaults to False. If you need to load secrets from environment variables:

python from langchaincore.load import load

obj = load(serializeddata, secretsfromenv=True)

Credits

Dumps bug was reported by @yardenporat Changes for security hardening due to findings from @0xn3va and @VladimirEliTokarev