GHSA-6qv9-48xg-fc7f LangChain Vulnerable to Template Injection via Attribute Access in Prompt Templates

• PyPI langchain-core: >= 1.0.0, < 1.0.7 → fixed in 1.0.7 • PyPI langchain-core: < 0.3.80 → fixed in 0.3.80

Context A template injection vulnerability exists in LangChain's prompt template system that allows attackers to access Python object internals through template syntax. This vulnerability affects applications that accept untrusted template strings (not just template variables) in `ChatPromptTemplate` and related prompt template classes. Templates allow attribute access (`.`) and indexing (`[]`) but not method invocation (`()`). The combination of attribute access and indexing may enable exploitation depending on which objects are passed to templates. When template variables are simple strings (the common case), the impact is limited. However, when using `MessagesPlaceholder` with chat message objects, attackers can traverse through object attributes and dictionary lookups (e.g., `__globals__`) to reach sensitive data such as environment variables. The vulnerability specifically requires that applications accept template strings (the structure) from untrusted sources, not just template variables (the data). Most applications either do not use templates or else use hardcoded templates and are not vulnerable. Affected Components • `langchain-core` package • Template formats: • F-string templates (`template_format="f-string"`) - Vulnerability fixed • Mustache templates (`template_format="mustache"`) - Defensive hardening • Jinja2 templates (`template_format="jinja2"`) - Defensive hardening Impact Attackers who can control template strings (not just template variables) can: • Access Python object attributes and internal properties via attribute traversal • Extract sensitive information from object internals (e.g., `__class__`, `__globals__`) • Potentially escalate to more severe attacks depending on the objects passed to templates Attack Vectors 1. F-string Template Injection Before Fix: ```python from langchain_core.prompts import ChatPromptTemplate malicious_template = ChatPromptTemplate.from_messages( [("human", "{msg.__class__.__name__}")], template_format="f-string" ) Note that this requires passing a placeholder variable for "msg.__class__.__name__". result = malicious_template.invoke({"msg": "foo", "msg.__class__.__name__": "safe_placeholder"}) Previously returned >>> result.messages[0].content >>> 'str' ``` 2. Mustache Template Injection Before Fix: ```python from langchain_core.prompts import ChatPromptTemplate from langchain_core.messages import HumanMessage msg = HumanMessage("Hello") Attacker controls the template string malicious_template = ChatPromptTemplate.from_messages( [("human", "{{question.__class__.__name__}}")], template_format="mustache" ) result = malicious_template.invoke({"question": msg}) Previously returned: "HumanMessage" (getattr() exposed internals) ``` 3. Jinja2 Template Injection Before Fix: ```python from langchain_core.prompts import ChatPromptTemplate from langchain_core.messages import HumanMessage msg = HumanMessage("Hello") Attacker controls the template string malicious_template = ChatPromptTemplate.from_messages( [("human", "{{question.parse_raw}}")], template_format="jinja2" ) result = malicious_template.invoke({"question": msg}) Could access non-dunder attributes/methods on objects ``` Root Cause 1. F-string templates: The implementation used Python's `string.Formatter().parse()` to extract variable names from template strings. This method returns the complete field expression, including attribute access syntax: ```python from string import Formatter template = "{msg.__class__} and {x}" print([var_name for (_, var_name, _, _) in Formatter().parse(template)]) # Returns: ['msg.__class__', 'x'] ``` The extracted names were not validated to ensure they were simple identifiers. As a result, template strings containing attribute traversal and indexing expressions (e.g., `{obj.__class__.__name__}` or `{obj.method.__globals__[os]}`) were accepted and subsequently evaluated during formatting. While f-string templates do not support method calls with `()`, they do support `[]` indexing, which could allow traversal through dictionaries like `__globals__` to reach sensitive objects. 2. Mustache templates: By design, used `getattr()` as a fallback to support accessing attributes on objects (e.g., `{{user.name}}` on a User object). However, we decided to restrict this to simpler primitives that subclass dict, list, and tuple types as defensive hardening, since untrusted templates could exploit attribute access to reach internal properties like class on arbitrary objects 3. Jinja2 templates: Jinja2's default `SandboxedEnvironment` blocks dunder attributes (e.g., `__class__`) but permits access to other attributes and methods on objects. While Jinja2 templates in LangChain are typically used with trusted template strings, as a defense-in-depth measure, we've restricted the environment to block all attribute and method access on objects passed to templates. Who Is Affected? High Risk Scenarios You are affected if your application: • Accepts template strings from untrusted sources (user input, external APIs, databases) • Dynamically constructs prompt templates based on user-provided patterns • Allows users to customize or create prompt templates Example vulnerable code: ```python User controls the template string itself user_template_string = request.json.get("template") # DANGEROUS prompt = ChatPromptTemplate.from_messages( [("human", user_template_string)], template_format="mustache" ) result = prompt.invoke({"data": sensitive_object}) ``` Low/No Risk Scenarios You are NOT affected if: • Template strings are hardcoded in your application code • Template strings come only from trusted, controlled sources • Users can only provide values for template variables, not the template structure itself Example safe code: ```python Template is hardcoded - users only control variables prompt = ChatPromptTemplate.from_messages( [("human", "User question: {question}")], # SAFE template_format="f-string" ) User input only fills the 'question' variable result = prompt.invoke({"question": user_input}) ``` The Fix F-string Templates F-string templates had a clear vulnerability where attribute access syntax was exploitable. We've added strict validation to prevent this: • Added validation to enforce that variable names must be valid Python identifiers • Rejects syntax like `{obj.attr}`, `{obj[0]}`, or `{obj.__class__}` • Only allows simple variable names: `{variable_name}` ```python After fix - these are rejected at template creation time ChatPromptTemplate.from_messages( [("human", "{msg.__class__}")], # ValueError: Invalid variable name template_format="f-string" ) ``` Mustache Templates (Defensive Hardening) As defensive hardening, we've restricted what Mustache templates support to reduce the attack surface: • Replaced `getattr()` fallback with strict type checking • Only allows traversal into `dict`, `list`, and `tuple` types • Blocks attribute access on arbitrary Python objects ```python After hardening - attribute access returns empty string prompt = ChatPromptTemplate.from_messages( [("human", "{{msg.__class__}}")], template_format="mustache" ) result = prompt.invoke({"msg": HumanMessage("test")}) Returns: "" (access blocked) ``` Jinja2 Templates (Defensive Hardening) As defensive hardening, we've significantly restricted Jinja2 template capabilities: • Introduced `_RestrictedSandboxedEnvironment` that blocks ALL attribute/method access • Only allows simple variable lookups from the context dictionary • Raises `SecurityError` on any attribute access attempt ```python After hardening - all attribute access is blocked prompt = ChatPromptTemplate.from_messages( [("human", "{{msg.content}}")], template_format="jinja2" ) Raises SecurityError: Access to attributes is not allowed ``` Important Recommendation: Due to the expre…