LangChain is a framework for building agents and LLM-powered applications. Prior to 0.3.85 and 1.3.3, LangChain contains older runtime code paths that deserialize run inputs, run outputs, or other application-controlled payloads using overly broad object allowlists. These paths may call load() with allowedobjects="all". This does not enable arbitrary Python object deserialization, but it does allow any trusted LangChain-serializable object to be revived, which is broader than these runtime paths require. As a result, attacker-supplied LangChain serialized constructor dictionaries may cause trusted runtime paths to instantiate classes with untrusted constructor arguments. This vulnerability is fixed in 0.3.85 and 1.3.3.

A vulnerability in langchain-core versions >=0.1.17,<0.1.53, >=0.2.0,<0.2.43, and >=0.3.0,<0.3.15 allows unauthorized users to read arbitrary files from the host file system. The issue arises from the ability to create langchaincore.prompts.ImagePromptTemplate's (and by extension langchaincore.prompts.ChatPromptTemplate's) with input variables that can read any user-specified path from the server file system. If the outputs of these prompt templates are exposed to the user, either directly or through downstream model outputs, it can lead to the exposure of sensitive information.

A vulnerability in the GraphCypherQAChain class of langchain-ai/langchain version 0.2.5 allows for SQL injection through prompt injection. This vulnerability can lead to unauthorized data manipulation, data exfiltration, denial of service (DoS) by deleting all data, breaches in multi-tenant security environments, and data integrity issues. Attackers can create, update, or delete nodes and relationships without proper authorization, extract sensitive data, disrupt services, access data across different tenants, and compromise the integrity of the database.

A vulnerability in the GraphCypherQAChain class of langchain-ai/langchainjs versions 0.2.5 and all versions with this class allows for prompt injection, leading to SQL injection. This vulnerability permits unauthorized data manipulation, data exfiltration, denial of service (DoS) by deleting all data, breaches in multi-tenant security environments, and data integrity issues. Attackers can create, update, or delete nodes and relationships without proper authorization, extract sensitive data, disrupt services, access data across different tenants, and compromise the integrity of the database.

A path traversal vulnerability exists in the getFullPath method of langchain-ai/langchainjs version 0.2.5. This vulnerability allows attackers to save files anywhere in the filesystem, overwrite existing text files, read .txt files, and delete files. The vulnerability is exploited through the setFileContent, getParsedFile, and mdelete methods, which do not properly sanitize user input.

Versions of the package langchain-experimental from 0.0.15 and before 0.0.21 are vulnerable to Arbitrary Code Execution when retrieving values from the database, the code will attempt to call 'eval' on all values. An attacker can exploit this vulnerability and execute arbitrary python code if they can control the input prompt and the server is configured with VectorSQLDatabaseChain.

Notes:

Impact on the Confidentiality, Integrity and Availability of the vulnerable component:

Confidentiality: Code execution happens within the impacted component, in this case langchain-experimental, so all resources are necessarily accessible.

Integrity: There is nothing protected by the impacted component inherently. Although anything returned from the component counts as 'information' for which the trustworthiness can be compromised.

Availability: The loss of availability isn't caused by the attack itself, but it happens as a result during the attacker's post-exploitation steps.

Impact on the Confidentiality, Integrity and Availability of the subsequent system:

As a legitimate low-privileged user of the package (PR:L) the attacker does not have more access to data owned by the package as a result of this vulnerability than they did with normal usage (e.g. can query the DB). The unintended action that one can perform by breaking out of the app environment and exfiltrating files, making remote connections etc. happens during the post exploitation phase in the subsequent system - in this case, the OS.

AT:P: An attacker needs to be able to influence the input prompt, whilst the server is configured with the VectorSQLDatabaseChain plugin.

A Denial-of-Service (DoS) vulnerability exists in the SitemapLoader class of the langchain-community package, affecting all versions. The parsesitemap method, responsible for parsing sitemaps and extracting URLs, lacks a mechanism to prevent infinite recursion when a sitemap URL refers to the current sitemap itself. This oversight allows for the possibility of an infinite loop, leading to a crash by exceeding the maximum recursion depth in Python. This vulnerability can be exploited to occupy server socket/port resources and crash the Python process, impacting the availability of services relying on this functionality.

A Server-Side Request Forgery (SSRF) vulnerability exists in the Web Research Retriever component in langchain-community (langchain-community.retrievers.webresearch.WebResearchRetriever). The vulnerability arises because the Web Research Retriever does not restrict requests to remote internet addresses, allowing it to reach local addresses. This flaw enables attackers to execute port scans, access local services, and in some scenarios, read instance metadata from cloud environments. The vulnerability is particularly concerning as it can be exploited to abuse the Web Explorer server as a proxy for web attacks on third parties and interact with servers in the local network, including reading their response data. This could potentially lead to arbitrary code execution, depending on the nature of the local services. The vulnerability is limited to GET requests, as POST requests are not possible, but the impact on confidentiality, integrity, and availability is significant due to the potential for stolen credentials and state-changing interactions with internal APIs.

The patched code: Requires users to opt-in Suggests using a proxy to prevent requests to local addresses

LangChain through 0.1.10 allows ../ directory traversal by an actor who is able to control the final part of the path parameter in a loadchain call. This bypasses the intended behavior of loading configurations only from the hwchase17/langchain-hub GitHub repository. The outcome can be disclosure of an API key for a large language model online service, or remote code execution.

A vulnerability was found in LangChain langchaincommunity 0.0.26. It has been classified as critical. Affected is the function loadlocal in the library libs/community/langchaincommunity/retrievers/tfidf.py of the component TFIDFRetriever. The manipulation leads to server-side request forgery. It is possible to launch the attack remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 0.0.27 is able to address this issue. It is recommended to upgrade the affected component. The identifier of this vulnerability is VDB-255372.

In Langchain through 0.0.155, prompt injection allows an attacker to force the service to retrieve data from an arbitrary URL, essentially providing SSRF and potentially injecting content into downstream tasks.

LangChain before 0.0.317 allows SSRF via documentloaders/recursiveurlloader.py because crawling can proceed from an external server to an internal server.

An issue in LanChain-ai Langchain v.0.0.245 allows a remote attacker to execute arbitrary code via the evaluate function in the numexpr library.

An issue in langchain v.0.0.171 allows a remote attacker to execute arbitrary code via a JSON file to loadprompt. This is related to subclasses or a template.

An issue in LangChain prior to v.0.0.247 allows a remote attacker to execute arbitrary code via the prompt parameter.

An issue in Harrison Chase langchain before version 0.0.236 allows a remote attacker to execute arbitrary code via the frommathprompt and fromcoloredobjectprompt functions.

An issue in langchain langchain-ai before version 0.0.325 allows a remote attacker to execute arbitrary code via a crafted script to the PythonAstREPLTool.run component.

In LangChain through 0.0.131, the LLMMathChain chain allows prompt injection attacks that can execute arbitrary code via the Python exec() method.