A Server-Side Request Forgery (SSRF) vulnerability exists in the RequestsToolkit component of the langchain-community package (specifically, langchaincommunity.agenttoolkits.openapi.toolkit.RequestsToolkit) in langchain-ai/langchain version 0.0.27. This vulnerability occurs because the toolkit does not enforce restrictions on requests to remote internet addresses, allowing it to also access local addresses. As a result, an attacker could exploit this flaw to perform port scans, access local services, retrieve instance metadata from cloud environments (e.g., Azure, AWS), and interact with servers on the local network. This issue has been fixed in version 0.0.28.

Langchain 0.0.171 is vulnerable to Arbitrary code execution in loadprompt.

langchainexperimental (aka LangChain Experimental) before 0.0.52, part of LangChain before 0.1.8, allows an attacker to bypass the CVE-2023-44467 fix and execute arbitrary code via the import, subclasses, builtins, globals, getattribute, bases, mro, or base attribute in Python code. These are not prohibited by palchain/base.py.

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.

An issue in langchain allows an attacker to execute arbitrary code via the PALChain in the python exec method.

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.

langchainexperimental (aka LangChain Experimental) 0.1.17 through 0.3.0 for LangChain allows attackers to execute arbitrary code through sympy.sympify (which uses eval) in LLMSymbolicMathChain. LLMSymbolicMathChain was introduced in fcccde406dd9e9b05fc9babcbeb9ff527b0ec0c6 (2023-10-05).

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.

langchain-ai v0.3.51 was discovered to contain an indirect prompt injection vulnerability in the GmailToolkit component. This vulnerability allows attackers to execute arbitrary code and compromise the application via a crafted email message. NOTE: this is disputed by the Supplier because the code-execution issue was introduced by user-written code that does not adhere to the LangChain security practices.

Insecure permissions in LangChain-ChatGLM-Webui commit ef829 allows attackers to arbitrarily view and download sensitive files via supplying a crafted request.

GHSA-fw9q-39r9-c252: Prototype Pollution via Incomplete Lodash set() Guard in langsmith-sdk

Severity: Medium (CVSS ~5.6) Status: Fixed in 0.5.18

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Summary

The LangSmith JavaScript/TypeScript SDK (langsmith) contains an incomplete prototype pollution fix in its internally vendored lodash set() utility. The baseAssignValue() function only guards against the proto key, but fails to prevent traversal via constructor.prototype. This allows an attacker who controls keys in data processed by the createAnonymizer() API to pollute Object.prototype, affecting all objects in the Node.js process.

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Affected Products

| Product | Affected Versions | Component | |---------|-------------------|-----------| | langsmith (npm) | <= 0.5.17 | js/src/utils/lodash/baseAssignValue.ts, js/src/anonymizer/index.ts | | langchain-ai/langsmith-sdk | GitHub main branch (as of 2026-03-24) | JS/TypeScript SDK |

Not affected: The Python SDK (langsmith on PyPI) does not use lodash or an equivalent pattern.

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Root Cause

The SDK vendors an internal copy of lodash's set() function at js/src/utils/lodash/. The baseAssignValue() function at baseAssignValue.ts:11 implements a guard for prototype pollution:

typescript function baseAssignValue(object: Record<string, any>, key: string, value: any) { if (key === "proto") { Object.defineProperty(object, key, { configurable: true, enumerable: true, value: value, writable: true, }); } else { object[key] = value; // ← No guard for "constructor" or "prototype" keys } }

This blocks proto pollution but does not block the constructor.prototype traversal path. When set() is called with a path like "constructor.prototype.polluted":

1. castPath() splits it into ["constructor", "prototype", "polluted"] 2. baseSet() iterates: obj.constructor → Object → Object.prototype 3. assignValue(Object.prototype, "polluted", value) calls baseAssignValue() 4. Key is "polluted" (not "proto"), so the guard is bypassed 5. Object.prototype.polluted = value — all objects are polluted

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Attack Vector via Anonymizer

The createAnonymizer() API (importable as langsmith/anonymizer) processes data by:

1. Extracting string nodes — extractStringNodes() walks an object recursively and builds dotted paths from keys 2. Applying regex replacements — If a string value matches a configured pattern, the node is marked for update (anonymizer/index.ts:95) 3. Writing back with set() — set(mutateValue, node.path, node.value) writes the replaced value back (anonymizer/index.ts:123)

An attacker who controls keys in data being anonymized can construct a nested object where the path resolves to constructor.prototype.X:

javascript { wrapper: { "constructor.prototype.isAdmin": "contains-secret-pattern" } }

extractStringNodes() produces path "wrapper.constructor.prototype.isAdmin". When the replacement triggers and set() writes back, it traverses up to Object.prototype.

Although createAnonymizer() uses deepClone() at anonymizer/index.ts:62 (JSON.parse(JSON.stringify(data))), the prototype chain traversal escapes the clone boundary because clone.wrapper.constructor resolves to the global Object constructor, not a cloned copy.

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Proof of Concept

javascript import { createAnonymizer } from "langsmith/anonymizer";

const anonymizer = createAnonymizer([ { pattern: "secret", replace: "[REDACTED]" } ]);

console.log("BEFORE:", ({}).isAdmin); // undefined

const maliciousInput = { wrapper: { "constructor.prototype.isAdmin": "this-is-secret-data" } };

anonymizer(maliciousInput);

console.log("AFTER:", ({}).isAdmin); // "this-is-[REDACTED]-data" console.log("Array:", [].isAdmin); // "this-is-[REDACTED]-data"

function checkAccess(user) { if (user.isAdmin) return "ACCESS GRANTED"; return "ACCESS DENIED"; } console.log(checkAccess({ name: "bob" })); // "ACCESS GRANTED" ← BYPASSED

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Impact

Prototype pollution in a Node.js process can enable:

1. Authentication bypass — if (user.isAdmin) checks succeed on all objects 2. Remote Code Execution — Exploitable in template engines (Pug, EJS, Handlebars, Nunjucks) via polluted prototype properties that reach eval()/Function() sinks 3. Denial of Service — Overwriting toString, valueOf, or hasOwnProperty on all objects 4. Data exfiltration — Polluting serialization methods to inject attacker-controlled values

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Remediation

In baseAssignValue.ts, extend the guard to cover constructor and prototype keys:

typescript function baseAssignValue(object, key, value) { if (key === "proto" || key === "constructor" || key === "prototype") { Object.defineProperty(object, key, { configurable: true, enumerable: true, value, writable: true, }); } else { object[key] = value; } }

As defense in depth, extractStringNodes() in anonymizer/index.ts should also sanitize or reject path segments matching constructor or prototype before passing them to set().

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Timeline

| Date | Event | |------|-------| | 2026-03-24 | Initial report submitted | | 2026-04-09 | Vendor confirmed; fixed in 0.5.18 |

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Credits

Reported by: OneThing4101

langchainexperimental (aka LangChain Experimental) in LangChain before 0.0.306 allows an attacker to bypass the CVE-2023-36258 fix and execute arbitrary code via import in Python code, which is not prohibited by palchain/base.py.

An issue in Harrison Chase langchain allows an attacker to execute arbitrary code via the PALChain,frommathprompt(llm).run in the python exec method.

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

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.

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.

Langchain 0.0.171 is vulnerable to Arbitrary Code Execution.

An issue in langchain allows a remote attacker to execute arbitrary code via the PALChain parameter in the Python exec method.

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, additionalkwargs, or responsemetadata 2. Class instantiation within trusted namespaces: Injected manifests could instantiate any Serializable subclass, but only within the pre-approved trusted namespaces (langchaincore, langchain, langchaincommunity). 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 allowedobjects="core" (restricted to serialization mappings), secretsfromenv changed from True to False, and default Jinja2 template blocking via initvalidator. These are breaking changes for some use cases.

Who is affected?

Applications are vulnerable if they:

1. Use astreamevents(version="v1") — The v1 implementation internally uses vulnerable serialization. Note: astreamevents(version="v2") is not vulnerable. 2. Use Runnable.astreamlog() — 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 createlcstore or createkvdocstore 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 additionalkwargs or responsemetadata, 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": ["ENVVAR"]} to load environment variables during deserialization (when secretsfromenv=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 secretsfromenv=True (the old default) - LLM responses in additionalkwargs can be controlled via prompt injection

Exploit example

python from langchaincore.load import dumps, load import os

Attacker injects secret structure into user-controlled data attackerdict = { "userdata": { "lc": 1, "type": "secret", "id": ["OPENAIAPIKEY"] } }

serialized = dumps(attackerdict) # Bug: does NOT escape the 'lc' key

os.environ["OPENAIAPIKEY"] = "sk-secret-key-12345" deserialized = load(serialized, secretsfromenv=True)

print(deserialized["userdata"]) # "sk-secret-key-12345" - SECRET LEAKED!

Security hardening changes (breaking changes)

This patch introduces three breaking changes to load() and loads():

1. New allowedobjects 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 langchaincore. We recommend that users explicitly specify which objects they want to allow for serialization/deserialization. 2. secretsfromenv default changed from True to False: Disables automatic secret loading from environment 3. New initvalidator parameter (defaults to defaultinitvalidator): 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:

python from langchaincore.load import load

Uses default allowlist from serialization mappings obj = load(serializeddata)

For custom classes

If you're deserializing custom classes not in the serialization mappings, add them to the allowlist:

python from langchaincore.load import load from mypackage import MyCustomClass

Specify the classes you need obj = load(serializeddata, allowedobjects=[MyCustomClass])

For Jinja2 templates

Jinja2 templates are now blocked by default because they can execute arbitrary code. If you need Jinja2 templates, pass initvalidator=None:

python from langchaincore.load import load from langchaincore.prompts import PromptTemplate

obj = load( serializeddata, allowedobjects=[PromptTemplate], initvalidator=None )

[!WARNING] Only disable initvalidator if you trust the serialized data. Jinja2 templates can execute arbitrary Python code.

For secrets from environment

secretsfromenv now defaults to False. If you need to load secrets from environment variables:

python from langchaincore.load import load

obj = load(serializeddata, secretsfromenv=True)

Credits

Dumps bug was reported by @yardenporat Changes for security hardening due to findings from @0xn3va and @VladimirEliTokarev

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.

Context

A serialization injection vulnerability exists in LangChain JS's toJSON() method (and subsequently when string-ifying objects using JSON.stringify(). The method did not escape objects with 'lc' keys when serializing free-form data in kwargs. 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 Serializable.toJSON(): this method failed to escape user-controlled objects containing 'lc' keys within kwargs (e.g., additionalkwargs, metadata, responsemetadata). When this unescaped data was later deserialized via load(), 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, additionalkwargs, or responsemetadata 2. Secret extraction: Injected secret structures could extract environment variables when secretsFromEnv was enabled (which had no explicit default, effectively defaulting to true behavior) 3. Class instantiation via import maps: Injected constructor structures could instantiate any class available in the provided import maps with attacker-controlled parameters

Note on import maps: Classes must be explicitly included in import maps to be instantiatable. The core import map includes standard types (messages, prompts, documents), and users can extend this via importMap and optionalImportsMap options. This architecture naturally limits the attack surface—an allowedObjects parameter is not necessary because users control which classes are available through the import maps they provide.

Security hardening: This patch fixes the escaping bug in toJSON() and introduces new restrictive defaults in load(): secretsFromEnv now explicitly defaults to false, and a maxDepth parameter protects against DoS via deeply nested structures. JSDoc security warnings have been added to all import map options.

Who is affected?

Applications are vulnerable if they:

1. Serialize untrusted data via JSON.stringify() on Serializable objects, then deserialize with load() — 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. 2. Deserialize untrusted data with load() — Directly deserializing untrusted data that may contain injected 'lc' structures. 3. Use LangGraph checkpoints — Checkpoint serialization/deserialization paths may be affected.

The most common attack vector is through LLM response fields like additionalkwargs or responsemetadata, 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": ["ENVVAR"]} to load environment variables during deserialization (when secretsFromEnv: true). They can also instantiate classes with controlled parameters by injecting constructor structures to instantiate any class within the provided import maps 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 secretsFromEnv: true - LLM responses in additionalkwargs can be controlled via prompt injection

Exploit example

typescript import { load } from "@langchain/core/load";

// Attacker injects secret structure into user-controlled data const attackerPayload = JSON.stringify({ userdata: { lc: 1, type: "secret", id: ["OPENAIAPIKEY"], }, });

process.env.OPENAIAPIKEY = "sk-secret-key-12345";

// With secretsFromEnv: true, the secret is extracted const deserialized = await load(attackerPayload, { secretsFromEnv: true });

console.log(deserialized.userdata); // "sk-secret-key-12345" - SECRET LEAKED!

Security hardening changes

This patch introduces the following changes to load():

1. secretsFromEnv default changed to false: Disables automatic secret loading from environment variables. Secrets not found in secretsMap now throw an error instead of being loaded from process.env. This fail-safe behavior ensures missing secrets are caught immediately rather than silently continuing with null. 2. New maxDepth parameter (defaults to 50): Protects against denial-of-service attacks via deeply nested JSON structures that could cause stack overflow. 3. Escape mechanism in toJSON(): User-controlled objects containing 'lc' keys are now wrapped in {"lcescaped": {...}} during serialization and unwrapped as plain data during deserialization. 4. JSDoc security warnings: All import map options (importMap, optionalImportsMap, optionalImportEntrypoints) now include security warnings about never populating them from user input.

Migration guide

No changes needed for most users

If you're deserializing standard LangChain types (messages, documents, prompts) using the core import map, your code will work without changes:

typescript import { load } from "@langchain/core/load";

// Works with default settings const obj = await load(serializedData);

For secrets from environment

secretsFromEnv now defaults to false, and missing secrets throw an error. If you need to load secrets:

typescript import { load } from "@langchain/core/load";

// Provide secrets explicitly (recommended) const obj = await load(serializedData, { secretsMap: { OPENAIAPIKEY: process.env.OPENAIAPIKEY }, });

// Or explicitly opt-in to load from env (only use with trusted data) const obj = await load(serializedData, { secretsFromEnv: true });

Warning: Only enable secretsFromEnv if you trust the serialized data. Untrusted data could extract any environment variable.

Note: If a secret reference is encountered but not found in secretsMap (and secretsFromEnv is false or the secret is not in the environment), an error is thrown. This fail-safe behavior ensures you're aware of missing secrets rather than silently receiving null values.

For deeply nested structures

If you have legitimate deeply nested data that exceeds the default depth limit of 50:

typescript import { load } from "@langchain/core/load";

const obj = await load(serializedData, { maxDepth: 100 });

For custom import maps

If you provide custom import maps, ensure they only contain trusted modules:

typescript import { load } from "@langchain/core/load"; import as myModule from "./my-trusted-module";

// GOOD - explicitly include only trusted modules const obj = await load(serializedData, { importMap: { mymodule: myModule }, });

// BAD - never populate from user input const obj = await load(serializedData, { importMap: userProvidedImports, // DANGEROUS! });

Summary

langgraph-sdk constructs HTTP request paths for resource operations by interpolating caller-supplied identifier values into URL templates. Without sanitization of those values, identifiers that contain characters with special meaning in URL paths could cause the resulting request to address a different resource (and potentially a different resource type) than the SDK method's call site indicates. In deployments where the SDK receives identifier values that originate from untrusted sources, this could result in unintended access, modification, or deletion of resources beyond the calling user's authorization scope.

This issue is most consequential in deployments that:

- forward end-user-supplied values directly into SDK identifier parameters without first validating them against an expected format (such as a UUID), and - rely on URL-prefix-based authorization at an upstream layer (reverse proxy, edge gateway, WAF), where the authorization decision is made on the SDK call's intended path rather than on the final delivered request path.

There have no evidence of this behavior being triggered in the wild. This change is intended to reduce the surface available when caller-supplied identifier values originate from untrusted sources.

Affected users / systems

You may be affected if you:

- use langgraph-sdk (Python) to address resources by identifier, and - pass identifier values into SDK methods that originate from end-user input, untrusted third-party callers, or any source that does not validate identifier format before the SDK call.

Applications that validate identifier values (for example, by parsing them as UUIDs and rejecting anything that does not parse) before passing them to SDK methods are not affected. Validated UUIDs round-trip through the SDK request path unchanged.

Impact

- Potential unintended access, modification, or deletion of resources via SDK methods called for a different resource type, when caller-supplied identifier values are not validated. - In deployments with prefix-based authorization at an upstream layer, the authorization decision and the final delivered request path may diverge. - Confidentiality: disclosure of resource content beyond the authorization scope of the calling user. - Integrity: modification or deletion of resources beyond the authorization scope of the calling user.

Patches / mitigation

The SDK now applies path-segment encoding to identifier values before they are interpolated into request URL templates. After this change, identifier values that contain characters with special meaning in URL paths are transmitted as encoded byte sequences and routed to the resource the SDK method's call site indicates.

Compatibility

Identifier values that match the standard UUID format, or any other format that contains only characters safe to transmit unencoded in URL path segments, round-trip through the SDK request path unchanged. Applications that already validate identifier inputs see no behavioral change.

Operational guidance

- Validate identifier values (typically as UUIDs) at the boundary where untrusted input enters the application, before passing them to SDK methods. - For deployments relying on URL-prefix-based authorization upstream of LangGraph, prefer authorization at the LangGraph server layer or on parsed-and-validated request paths rather than on raw URL prefixes.

LangSmith / hosted deployments note

This issue affects the SDK that runs in caller applications. The LangGraph server runtime, including LangSmith-hosted deployments, receives ordinary HTTP requests on documented routes and is not itself affected by this issue. Applications that consume LangSmith-hosted services via langgraph-sdk and pass untrusted identifier values to SDK methods should upgrade.

First reported by: pucagit (CyStack).

langchain-ai/langchain is vulnerable to path traversal due to improper limitation of a pathname to a restricted directory ('Path Traversal') in its LocalFileStore functionality. An attacker can leverage this vulnerability to read or write files anywhere on the filesystem, potentially leading to information disclosure or remote code execution. The issue lies in the handling of file paths in the mset and mget methods, where user-supplied input is not adequately sanitized, allowing directory traversal sequences to reach unintended directories.

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

LangChain versions up to and including 0.3.1 contain a regular expression denial-of-service (ReDoS) vulnerability in the MRKLOutputParser.parse() method (libs/langchain/langchain/agents/mrkl/outputparser.py). The parser applies a backtracking-prone regular expression when extracting tool actions from model output. An attacker who can supply or influence the parsed text (for example via prompt injection in downstream applications that pass LLM output directly into MRKLOutputParser.parse()) can trigger excessive CPU consumption by providing a crafted payload, causing significant parsing delays and a denial-of-service condition.

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.

Langchain Helm Charts are Helm charts for deploying Langchain applications on Kubernetes. Prior to langchain-ai/helm version 0.12.71, a URL parameter injection vulnerability existed in LangSmith Studio that could allow unauthorized access to user accounts through stolen authentication tokens. The vulnerability affected both LangSmith Cloud and self-hosted deployments. Authenticated LangSmith users who clicked on a specially crafted malicious link would have their bearer token, user ID, and workspace ID transmitted to an attacker-controlled server. With this stolen token, an attacker could impersonate the victim and access any LangSmith resources or perform any actions the user was authorized to perform within their workspace. The attack required social engineering (phishing, malicious links in emails or chat applications) to convince users to click the crafted URL. The stolen tokens expired after 5 minutes, though repeated attacks against the same user were possible if they could be convinced to click malicious links multiple times. The fix in version 0.12.71 implements validation requiring user-defined allowed origins for the baseUrl parameter, preventing tokens from being sent to unauthorized servers. No known workarounds are available. Self-hosted customers must upgrade to the patched version.