Use astream_events(version="v1") — The v1 implementation internally uses vulnerable serialization. Note: astream_events(version="v2") is not vulnerable.
Use Runnable.astream_log() — This method internally uses vulnerable serialization for streaming outputs.
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.
Deserialize untrusted data with load() or loads() — Directly deserializing untrusted data that may contain injected 'lc' structures.
Use RunnableWithMessageHistory — Internal serialization in message history handling.
Use InMemoryVectorStore.load() to deserialize untrusted documents.
Load untrusted generations from cache using langchain-community caches.
Load untrusted manifests from the LangChain Hub via hub.pull.
Use StringRunEvaluatorChain on untrusted runs.
Use create_lc_store or create_kv_docstore with untrusted documents.
Use MultiVectorRetriever with byte stores containing untrusted documents.
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 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 secrets_from_env=True (the old default)
LLM responses in additional_kwargs can be controlled via prompt injection
Exploit example
from langchain_core.load import dumps, load
import os
# Attacker injects secret structure into user-controlled data
attacker_dict = {
"user_data": {
"lc": 1,
"type": "secret",
"id": ["OPENAI_API_KEY"]
}
}
serialized = dumps(attacker_dict) # Bug: does NOT escape the 'lc' key
os.environ["OPENAI_API_KEY"] = "sk-secret-key-12345"
deserialized = load(serialized, secrets_from_env=True)
print(deserialized["user_data"]) # "sk-secret-key-12345" - SECRET LEAKED!
Security hardening changes (breaking changes)
This patch introduces three breaking changes to load() and loads():
New allowed_objects 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 langchain_core. We recommend that users explicitly specify which objects they want to allow for serialization/deserialization.
secrets_from_env default changed from True to False: Disables automatic secret loading from environment
New init_validator parameter (defaults to default_init_validator): 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:
from langchain_core.load import load
# Uses default allowlist from serialization mappings
obj = load(serialized_data)
For custom classes
If you're deserializing custom classes not in the serialization mappings, add them to the allowlist:
from langchain_core.load import load
from my_package import MyCustomClass
# Specify the classes you need
obj = load(serialized_data, allowed_objects=[MyCustomClass])
For Jinja2 templates
Jinja2 templates are now blocked by default because they can execute arbitrary code. If you need Jinja2 templates, pass init_validator=None:
from langchain_core.load import load
from langchain_core.prompts import PromptTemplate
obj = load(
serialized_data,
allowed_objects=[PromptTemplate],
init_validator=None
)
[!WARNING]
Only disable init_validator if you trust the serialized data. Jinja2 templates can execute arbitrary Python code.
For secrets from environment
secrets_from_env now defaults to False. If you need to load secrets from environment variables:
from langchain_core.load import load
obj = load(serialized_data, secrets_from_env=True)
Credits
Dumps bug was reported by @yardenporat
Changes for security hardening due to findings from @0xn3va and @VladimirEliTokarev