Today we’re sitting down with Rupert Marais, our in-house security specialist, to dissect a critical vulnerability that’s sending ripples through the AI development community. Known as LangGrinch, this flaw in the popular LangChain framework highlights the dangerous intersection where large language models meet classic application security risks. Rupert will guide us through how this high-severity vulnerability, with a CVSS score of 9.3, allows attackers to steal secrets, the nuanced steps for proper remediation, and what this event teaches us about the foundational security challenges in building AI-powered applications.
The LangGrinch vulnerability, CVE-2025-68664, centers on the special ‘lc’ key in LangChain’s serialization. Could you walk us through, step-by-step, how an attacker exploits the dumps() function to turn user data into a trusted LangChain object and extract environment variable secrets?
Absolutely. The entire attack hinges on a breakdown of trust within the LangChain framework. Internally, LangChain uses a special dictionary key, ‘lc’, to mark its own serialized objects. Think of it as an internal badge that says, “I’m a legitimate, trusted component.” The vulnerability, at its core, is that the dumps() function failed to properly check and escape user-controlled data. So, an attacker can craft a piece of data—what should be just plain text or a simple dictionary—and embed that ‘lc’ key structure within it. When the application serializes this malicious data and later deserializes it, the system is tricked. It sees the ‘lc’ badge and treats the attacker’s data not as untrusted user input, but as a legitimate LangChain object with all the privileges that entails. Before the patch, the situation was even more dangerous because the “secrets_from_env” setting was true by default, meaning this maliciously instantiated object could be instructed to simply read and expose sensitive secrets directly from the system’s environment variables.
Researcher Yarden Porat identified prompt injection into fields like additional_kwargs as a key vector. Can you share a concrete example of an attack chain where this technique escalates to instantiating an unsafe object within trusted namespaces, and what the ultimate business impact could be?
This is where the theoretical vulnerability becomes a terrifying reality. Imagine an application that uses an LLM to process user feedback and stores some metadata alongside it. An attacker could submit feedback containing a carefully crafted prompt injection. This prompt tricks the LLM into generating a response that includes a malicious payload hidden inside a seemingly benign field like additional_kwargs or response_metadata. The application, unaware, receives this response. Later, perhaps during a logging or streaming process, it serializes this entire object, including the hidden payload. When that data is eventually deserialized elsewhere in the system, the ‘lc’ key trick is triggered. The system then instantiates an unsafe object, not just anywhere, but within pre-approved, trusted namespaces like langchain_core or langchain_community. The ultimate business impact is catastrophic. This object could be designed to scan and exfiltrate all environment variables—API keys, database connection strings, private certificates—leading to a complete system compromise and a devastating data breach.
The patch for CVE-2025-68664 introduced new restrictive defaults, like the allowed_objects parameter. Beyond just updating, what is the step-by-step process a development team should follow to audit their existing code and correctly implement these new, stricter controls without breaking functionality?
Simply updating the package is only the first step and won’t fully protect you without further action. The patch fundamentally changes LangChain’s security posture to be secure by default. First, a team needs to update to the patched versions, like 1.2.5 or 0.3.81. Immediately after, they must begin a thorough code audit. The primary task is to find every single call to the load() and loads() functions. For each instance, they need to analyze exactly what kind of objects the application expects to deserialize and create an explicit allowlist for them using the new allowed_objects parameter. This prevents any unexpected or malicious objects from being instantiated. Secondly, they must review any logic that relied on secrets being loaded from the environment, because the secrets_from_env option is now set to False by default. Any feature that depended on this will break and must be refactored. Finally, they need to be aware that Jinja2 templates are also now blocked by default. It’s a manual, deliberate process of tightening security by moving from a model of implicit trust to one of explicit, minimal permissions.
A nearly identical flaw, CVE-2025-68665, was found in LangChain.js. What does this parallel vulnerability across Python and JavaScript ecosystems tell us about the foundational security challenges in framework design, especially when handling LLM outputs as a form of untrusted input?
The existence of a parallel flaw in LangChain.js, with a CVSS of 8.6, is a major red flag. It tells us this wasn’t an isolated implementation bug in a single Python function; it was a systemic, architectural flaw in the very design philosophy of the framework. The core concept of using an ‘lc’ key to denote trusted objects was replicated across both the Python and JavaScript ecosystems without building the necessary security guardrails around it. This highlights a critical and widespread challenge in the current AI gold rush: developers and frameworks are not consistently treating LLM output as a form of untrusted, user-controlled input. Any data coming from an LLM can be manipulated via prompt injection. When a framework builds core functionalities like serialization around the assumption that this data is safe, you get critical vulnerabilities like this. It proves that security principles must be baked into the design from day one, across all languages and platforms, not bolted on as an afterthought.
Do you have any advice for our readers?
My strongest piece of advice is to internalize what the researcher Yarden Porat so perfectly stated: “LLM output is an untrusted input.” We are at a crucial intersection of AI and classic security, and organizations get caught off guard because they view the LLM as a trusted partner. You must treat every single string, dictionary, or piece of metadata returned by a model with the same level of suspicion you would an unvalidated web form submitted by an anonymous user. This means implementing rigorous input validation, sanitization, and leveraging secure-by-default configurations in every library you use. Do not trust that the model’s output is benign. The LangGrinch vulnerability is a perfect example of how an attacker can weaponize that trust to turn an AI’s response into a key that unlocks your most valuable secrets.
