NPM: LangSmith SDK: Public prompt pull deserializes untrusted manifests without trust boundary warning

NPM: LangSmith SDK: Public prompt pull deserializes untrusted manifests without trust boundary warning vulnerability discovered by ? in WordPress Npm langsmith versions 0.6.0...

Deserialization of Untrusted Data

Overview langsmith is a Client library to connect to the LangSmith Observability and Evaluation Platform. Affected versions of this package are vulnerable to Deserialization of Untrusted Data when fetching and processing prompt manifests from external sources. An attacker can execute arbitrary co...

Deserialization of Untrusted Data

Overview langsmith is a Client library to connect to the LangSmith Observability and Evaluation Platform. Affected versions of this package are vulnerable to Deserialization of Untrusted Data when fetching and processing prompt manifests from external sources. An attacker can execute arbitrary co...

Deserialization of Untrusted Data

Overview langchain is a Building applications with LLMs through composability Affected versions of this package are vulnerable to Deserialization of Untrusted Data when fetching and processing prompt manifests from external sources. An attacker can execute arbitrary code or manipulate application...

LangSmith SDK: Public prompt pull deserializes untrusted manifests without trust boundary warning

Description The LangSmith SDK's prompt pull methods pullprompt / pullpromptcommit in Python, pullPrompt / pullPromptCommit in JS/TS fetch and deserialize prompt manifests from the LangSmith Hub. These manifests may contain serialized LangChain objects and model configuration that affect runtime...

Deserialization of Untrusted Data

Overview langchain-classic is a Building applications with LLMs through composability Affected versions of this package are vulnerable to Deserialization of Untrusted Data when fetching and processing prompt manifests from external sources. An attacker can execute arbitrary code or manipulate...

CVE-2026-42203

A flaw was found in LiteLLM, an AI Gateway. An authenticated user could exploit this by sending a crafted prompt template to the POST /prompts/test endpoint. The endpoint rendered user-supplied prompt templates without proper sandboxing. This could lead to arbitrary code execution within the...

PT-2026-40723

Name of the Vulnerable Software and Affected Versions LangSmith SDK Python versions prior to 0.8.0 LangSmith SDK JS/TS versions prior to 0.6.0 Description The prompt pull methods pull prompt and pull prompt commit in Python, and pullPrompt and pullPromptCommit in JS/TS, fetch and deserialize prom...

PT-2026-40841

Four CVEs CVE-2026-29103, CVE-2026-29104, CVE-2026-29892, CVE-2026-30441 shared the same root cause. An MCP server's response to the client includes free-form text fields — tool descriptions, resource summaries, prompt argument hints. These fields are surfaced into the…...

CVE-2026-28993

This issue was addressed by adding an additional prompt for user consent. This issue is fixed in iOS 18.7.9 and iPadOS 18.7.9, iOS 26.5 and iPadOS 26.5, macOS Sequoia 15.7.7, macOS Sonoma 14.8.7, macOS Tahoe 26.5, visionOS 26.5. An app may be able to access user-sensitive data...

GHSA-M77W-P5JJ-XMHG OpenClaude Sandbox Bypass via Model-Controlled `dangerouslyDisableSandbox` Input

Summary The dangerouslyDisableSandbox parameter is exposed as part of the BashTool input schema, meaning the LLM an untrusted principal per the project's own threat model can set it to true in any tooluse response. Combined with the default allowUnsandboxedCommands: true setting, a prompt-injecte...

Directory Traversal

Overview Affected versions of this package are vulnerable to Directory Traversal via the createmodelversion function. An attacker can access arbitrary files on the server's filesystem by including the mlflow.prompt.isprompt tag in a CreateModelVersion request, which bypasses source path validatio...

Spring AI: Prompt Injection via Memory Poisoning in PromptChatMemoryAdvisor

A malicious user could craft input that is stored in conversation memory and later interpreted by the model in an unintended way. Applications using the affected advisor with user-controlled input may be susceptible to manipulation of model behavior across conversation turns...

GHSA-5852-PHMH-8FHR Spring AI: Prompt Injection via Memory Poisoning in PromptChatMemoryAdvisor

A malicious user could craft input that is stored in conversation memory and later interpreted by the model in an unintended way. Applications using the affected advisor with user-controlled input may be susceptible to manipulation of model behavior across conversation turns...

CVE-2026-41713 Prompt Injection via Memory Poisoning in PromptChatMemoryAdvisor

A malicious user could craft input that is stored in conversation memory and later interpreted by the model in an unintended way. Applications using the affected advisor with user-controlled input may be susceptible to manipulation of model behavior across conversation turns...

CVE-2026-41713 Prompt Injection via Memory Poisoning in PromptChatMemoryAdvisor

A malicious user could craft input that is stored in conversation memory and later interpreted by the model in an unintended way. Applications using the affected advisor with user-controlled input may be susceptible to manipulation of model behavior across conversation turns...

CVE-2026-41713

CVE-2026-41713 describes a prompt-injection style vulnerability in the PromptChatMemoryAdvisor where user-supplied input is stored in conversation memory and later interpreted by the model in an unintended way. Affected functionality is the advisor component that relies on memory of prior turns; ...

CVE-2026-41713 Prompt Injection via Memory Poisoning in PromptChatMemoryAdvisor

A malicious user could craft input that is stored in conversation memory and later interpreted by the model in an unintended way. Applications using the affected advisor with user-controlled input may be susceptible to manipulation of model behavior across conversation turns...

Malicious code in crazehub (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: amazon-inspector 53d37c0e75f63e9da7adcc1f71f8b67a665d080342df6857a15dadc297e4f075 crazehub/init.py performs multiple user-hostile actions at import time. Lines 2-3 unconditionally run os.system"pip install phonenumbers" and...

IPI-Proxy: An Intercepting Proxy for Red-Teaming Web-Browsing AI Agents against Indirect Prompt Injection

Web-browsing AI agents are increasingly deployed in enterprise settings under strict whitelists of approved domains, yet adversaries can still influence them by embedding hidden instructions in the HTML pages those domains serve. Existing red-teaming resources fall short of this scenario:...