ChainLeak Vulnerabilities Endangering Cloud Infrastructure

In early 2026, security researchers discovered a set of dangerous, wide-reaching vulnerabilities in a widely used open-source AI development framework called Chainlit. These vulnerabilities, collectively dubbed ChainLeak, threatened to expose sensitive data and even allow attackers to breach cloud environments, compromising enterprise infrastructure worldwide.

The discovery shone a spotlight on the growing risks inherent in modern AI application stacks and highlighted how common software flaws, when embedded in AI frameworks, can have cascading consequences.

Chainlit is designed to accelerate the development of conversational AI applications. It provides a ready-made web user interface, backend tools, session handling mechanisms, and built-in support for authentication and cloud deployment.

With hundreds of thousands of monthly downloads and millions of annual installs on the Python Package Index (PyPI), it has become a foundational component in many enterprise and academic AI systems. These tools enable developers to focus on building value for users. But, as the ChainLeak episode demonstrated, shortcuts in security can undermine that value entirely.

The ChainLeak flaws arose from weaknesses in how Chainlit processed custom elements. These elements are the framework's building blocks used by developers.

The weaknesses consisted of two distinct but related vulnerabilities:

• Arbitrary File Read (CVE-2026-22218): An authenticated attacker could submit a malicious custom element with a manipulated path property. Chainlit would then copy the file at that path into the attacker's session without validating the input. This failure allowed attackers to read any file on the server that the Chainlit process could access. Files included sensitive configuration files, source code, databases, and even cloud credentials.
• Server-Side Request Forgery (CVE-2026-22219): When deployed with the SQLAlchemy (a popular Python-based database toolkit) data layer, Chainlit allowed attackers to specify a URL in a custom element. The framework would follow this URL and fetch its contents. Because the request was executed from the server, attackers could reach internal network endpoints, cloud metadata services, and other protected services that should have been inaccessible from the outside.

Together, these two issues formed a potent attack chain. An attacker could first abuse the arbitrary file read flaw to harvest environment variables, credential files, and API tokens. Then, the attacker could use the SSRF bug to explore and pivot further into the network. Once inside, attackers were not limited to the AI application. They could leverage stolen credentials to access related cloud infrastructure, including storage buckets, internal APIs, or compute instances.

Why ChainLeak is Particularly Dangerous

The consequences of ChainLeak were not merely hypothetical. Zafran Labs researchers validated the vulnerabilities against real, internet-facing installations used by major organizations. They found that both flaws could be triggered without any user interaction. Attackers could exploit them remotely by sending crafted requests to exposed servers. In effect, Chainlit installations that were not properly updated were vulnerable to compromise with minimal effort.

The arbitrary file read vulnerability stood out because it enabled attackers to retrieve:

• Secrets like API keys and cloud credentials are stored in environment variables
• Session and configuration files
• Internal databases, including SQLite stores, that capture historical user queries and AI responses
• Application source code that could reveal further weaknesses in custom logic

Perhaps most alarming was the potential for this data to enable broader attacks. For example, attackers could extract authentication-signing secrets and user identifiers to forge valid tokens, effectively taking over user accounts associated with the AI application.

Cloud IAM roles or API keys discovered through the server environment could grant far deeper access, enabling lateral movement across cloud infrastructure. The SSRF vulnerability, while less immediately obvious, provided another escalation path. In cloud contexts, especially Amazon Web Services, metadata endpoints expose role credentials to instances.

By exploiting the SSRF flaw, attackers could potentially harvest these credentials and then use them to compromise additional resources, bypassing many traditional perimeter defenses.

Upon discovering the ChainLeak flaws in November 2025, Zafran Labs researchers followed responsible disclosure practices. They reported the vulnerabilities to Chainlit's maintainers on November 23, and the maintainers acknowledged them on December 9.

By December 24, 2025, Chainlit version 2.9.4 was released with fixes for both CVE-2026-22218 and CVE-2026-22219. Users and organizations were urged to upgrade immediately to mitigate the risks. By mid-January 2026, the vulnerabilities were officially catalogued and published in vulnerability tracking databases.

The response underscored a fundamental truth in modern software engineering: no code is too low-risk to ignore. Frameworks designed to simplify development can become attack vectors if proper validation, access controls, and security testing are not enforced. The rapid identification, disclosure, and patching of ChainLeak likely prevented widespread exploitation. It also served as a warning to the industry about emerging threats tied to AI infrastructure.

The ChainLeak incident revealed systemic issues in how AI applications are built. Traditional software stacks have well-understood security boundaries. In contrast, AI applications often involve interconnected layers. These include user interfaces, orchestration logic, large language model integration, and external tools. Frameworks like Chainlit sit at the intersection of these layers. A flaw in one component can ripple across the entire stack.

Developers and system administrators now recognize that AI frameworks are not merely convenience layers, but core system components whose security posture must be scrutinized with the same rigor as any other critical infrastructure.

In the broader context, ChainLeak accelerated conversations about how to build secure AI platforms, not just secure models. In a rapidly growing ecosystem, vigilance, collaboration, and proactive research are indispensable to protect sensitive data and preserve trust in AI technologies.