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CVE-2025-9556 is a critical vulnerability (CVSS 9.8) in LangChainGo that puts large language model (LLM) applications at significant risk. Exploitation could allow attackers to compromise LLM-based systems. Immediate patching is recommended for all affected deployments.
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German CERT advisories disclosed two vulnerabilities in **LangChain**, warning that the framework is affected by flaws that can lead to **information disclosure** and the **bypassing of security measures**. The issues were published in separate notices, identified as `2026-0877` and `2026-1010`, indicating multiple security weaknesses affecting the widely used LLM application framework. The advisories provide limited public detail, but the reported impact suggests attackers could expose sensitive data and circumvent protections built into LangChain-based deployments. Organizations using LangChain should review the affected advisories, identify exposed implementations, and prioritize vendor guidance, patching, and compensating controls to reduce the risk of data exposure and weakened application security.
Jun 29, 2026
Two critical serialization injection vulnerabilities were discovered in the LangChain framework, which is widely used for building LLM-powered applications. The first vulnerability (CVE-2025-68665) affects the `toJSON()` method in LangChain JS and related serialization routines, where user-controlled data containing the reserved `lc` key could be misinterpreted as legitimate LangChain objects during deserialization. The second vulnerability (CVE-2025-68664) impacts the `dumps()` and `dumpd()` functions, allowing attacker-supplied dictionaries with the `lc` key to be treated as internal objects, potentially leading to the extraction of secrets or the instantiation of internal classes with attacker-defined parameters. Both vulnerabilities are remotely exploitable and have been patched in recent versions of LangChain and LangChain Core. Exploitation of these flaws could allow attackers to extract sensitive information such as environment variables or manipulate application behavior by injecting malicious data structures. Organizations using affected versions of LangChain are strongly advised to upgrade to the patched releases—@langchain/core versions 0.3.80 and 1.1.8, langchain versions 0.3.37 and 1.2.3 for CVE-2025-68665, and langchain-core 0.3.81 and 1.2.5 for CVE-2025-68664—to mitigate the risk of exploitation.
Jun 29, 2026
Two high-severity vulnerabilities were disclosed in widely used AI application components, affecting **Langflow** and **vLLM**. In Langflow, `CVE-2026-33497` impacts versions before **1.7.1** and stems from improper filtering of `folder_name` and `file_name` in the `/profile_pictures/{folder_name}/{file_name}` endpoint. The path traversal flaw (`CWE-22`) allows unauthenticated attackers to read files across directories, including the application's `secret_key`, creating a direct risk of secret exposure and follow-on compromise. The issue is addressed in **Langflow 1.7.1** and tracked in GitHub advisory `GHSA-ph9w-r52h-28p7`. A separate flaw in vLLM, `CVE-2026-27893`, can lead to **remote code execution** by bypassing a user's attempt to disable remote code trust. In versions from **0.10.1** up to but not including **0.18.0**, two model implementation files hardcoded `trust_remote_code=True`, overriding the safer `--trust-remote-code=False` setting and allowing malicious model repositories to run code during model use. The vulnerability, classified as `CWE-693`, was patched in **vLLM 0.18.0**, underscoring supply-chain and configuration-bypass risks in AI infrastructure components.
Jun 29, 2026