LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.84 and 1.2.28, LangChains f-string prompt-template validation was incomplete in two respects. First, some prompt template classes accepted f-string templates and formatted them without enforcing the same attribute-access validation as PromptTemplate. In particular, DictPromptTemplate and ImagePromptTemplate could accept templates containing attribute access or indexing expressions and subsequently evaluate those expressions during formatting. Second, f-string validation based on parsed top-level field names did not reject nested replacement fields inside format specifiers. In this pattern, the nested replacement field appears in the format specifier rather than in the top-level field name. As a result, earlier validation based on parsed field names did not reject the template even though Python formatting would still attempt to resolve the nested expression at runtime. This vulnerability is fixed in 0.3.84 and 1.2.28.
Weakness
The product uses a template engine to insert or process externally-influenced input, but it does not neutralize or incorrectly neutralizes special elements or syntax that can be interpreted as template expressions or other code directives when processed by the engine.
Affected Software
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Langchain_core | Langchain | * | 0.3.84 (excluding) |
| Langchain_core | Langchain | 1.0.0 (including) | 1.2.28 (excluding) |
Extended Description
Many web applications use template engines that allow developers to insert externally-influenced values into free text or messages in order to generate a full web page, document, message, etc. Such engines include Twig, Jinja2, Pug, Java Server Pages, FreeMarker, Velocity, ColdFusion, Smarty, and many others - including PHP itself. Some CMS (Content Management Systems) also use templates. Template engines often have their own custom command or expression language. If an attacker can influence input into a template before it is processed, then the attacker can invoke arbitrary expressions, i.e. perform injection attacks. For example, in some template languages, an attacker could inject the expression “{{7*7}}” and determine if the output returns “49” instead. The syntax varies depending on the language. In some cases, XSS-style attacks can work, which can obscure the root cause if the developer does not closely investigate the root cause of the error. Template engines can be used on the server or client, so both “sides” could be affected by injection. The mechanisms of attack or the affected technologies might be different, but the mistake is fundamentally the same.
Potential Mitigations
References
- https://github.com/langchain-ai/langchain/commit/6bab0ba3c12328008ddca3e0d54ff5a6151cd27b
- https://github.com/langchain-ai/langchain/commit/af2ed47c6f008cdd551f3c0d87db3774c8dfe258
- https://github.com/langchain-ai/langchain/pull/36612
- https://github.com/langchain-ai/langchain/pull/36613
- https://github.com/langchain-ai/langchain/releases/tag/langchain-core%3D%3D0.3.84
- https://github.com/langchain-ai/langchain/releases/tag/langchain-core%3D%3D1.2.28
- https://github.com/langchain-ai/langchain/security/advisories/GHSA-926x-3r5x-gfhw