CVE-2025-68664

LangChain is a framework for building agents and LLM-powered applications. Prior to versions 0.3.81 and 1.2.5, 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. This issue has been patched in versions 0.3.81 and 1.2.5.

LangChain is a framework for building agents and LLM-powered applications. Prior to versions 0.3.81 and 1.2.5, 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. This issue has been patched in versions 0.3.81 and 1.2.5.

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), 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…

LangChain is a framework for building agents and LLM-powered applications. Prior to versions 0.3.81 and 1.2.5, 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. This issue has been patched in versions 0.3.81 and 1.2.5.

LangChain is a framework for building agents and LLM-powered applications. Prior to versions 0.3.81 and 1.2.5, 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. This issue has been patched in versions 0.3.81 and 1.2.5.

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), 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…

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), 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…

Root has patched CVE-2025-68664 in the langchain-core package for Root:PyPI. Multiple fixed versions available.