Chainlit Security Flaws Highlight Infrastructure Risks in AI Apps
Summary:
Chainlit, a widely adopted Python framework for building conversational AI applications, was found to contain two critical backend vulnerabilities that allow for Arbitrary File Read and Server-Side Request Forgery (SSRF). These flaws do not target the AI models themselves but rather the infrastructure that supports them.
The first vulnerability, CVE-2026-22218, enables authenticated users to manipulate the "project element" update flow to copy local server files into their session and subsequently download them.
The second, CVE-2026-22219, affects deployments utilizing a SQLAlchemy data layer and allows attackers to force the server to make requests to arbitrary URLs. The server then stores the responses from these requests, which can be retrieved through standard application API calls. Both issues stem from a lack of input validation when the backend processes user-controlled properties.
Security Officer Comments:
Chainlit is widely used by a variety of organizations, ranging from small startups to large enterprises and academic institutions. Because it is a Python-based framework designed to simplify the creation of conversational AI interfaces.
Zafran Research, which discovered the vulnerabilities, noted that many of the exposed servers they analyzed belonged to enterprises. These organizations use Chainlit to create internal interfaces where employees can interact with sensitive corporate data through LLMs (e.g., querying HR documents or technical manuals).
The potential impact of these vulnerabilities is severe, particularly for enterprise and cloud-hosted AI deployments. By exploiting the arbitrary file read flaw, an attacker can exfiltrate sensitive configuration files, environment variables, and local databases. This frequently leads to the disclosure of API keys, cloud service credentials, and authentication secrets, which can be used for lateral movement within a cloud environment.
Because Chainlit often caches user prompts and model responses, an attacker could gain access to entire conversation histories or proprietary data used to fine-tune the AI. The SSRF vulnerability compounds this risk by allowing attackers to probe internal network services and access cloud metadata services (such as AWS IMDS), which can facilitate the theft of temporary IAM role credentials and lead to a full compromise of the hosting cloud account.
Suggested Corrections:
The primary mitigation for these vulnerabilities is to upgrade Chainlit to version 2.9.4 or later, which contains the necessary security patches released on December 24, 2025. Network defenders should immediately audit their environments for any instances of Chainlit and ensure they are not running versions prior to 2.9.4.
In scenarios where patching cannot be performed immediately, it is recommended to implement Web Application Firewall (WAF) rules to monitor and block suspicious activity directed at the /project/element and /project/file/ endpoints, specifically looking for path traversal sequences (e.g., ../) or unauthorized external URLs.
Additionally, defenders should apply the principle of least privilege to the service accounts running AI applications, ensuring the process has no access to sensitive system files or internal management APIs that are not strictly necessary for its operation.
Link(s):
https://www.vulncheck.com/advisories/chainlit-arbitrary-file-read-via-project-element
Chainlit, a widely adopted Python framework for building conversational AI applications, was found to contain two critical backend vulnerabilities that allow for Arbitrary File Read and Server-Side Request Forgery (SSRF). These flaws do not target the AI models themselves but rather the infrastructure that supports them.
The first vulnerability, CVE-2026-22218, enables authenticated users to manipulate the "project element" update flow to copy local server files into their session and subsequently download them.
The second, CVE-2026-22219, affects deployments utilizing a SQLAlchemy data layer and allows attackers to force the server to make requests to arbitrary URLs. The server then stores the responses from these requests, which can be retrieved through standard application API calls. Both issues stem from a lack of input validation when the backend processes user-controlled properties.
Security Officer Comments:
Chainlit is widely used by a variety of organizations, ranging from small startups to large enterprises and academic institutions. Because it is a Python-based framework designed to simplify the creation of conversational AI interfaces.
Zafran Research, which discovered the vulnerabilities, noted that many of the exposed servers they analyzed belonged to enterprises. These organizations use Chainlit to create internal interfaces where employees can interact with sensitive corporate data through LLMs (e.g., querying HR documents or technical manuals).
The potential impact of these vulnerabilities is severe, particularly for enterprise and cloud-hosted AI deployments. By exploiting the arbitrary file read flaw, an attacker can exfiltrate sensitive configuration files, environment variables, and local databases. This frequently leads to the disclosure of API keys, cloud service credentials, and authentication secrets, which can be used for lateral movement within a cloud environment.
Because Chainlit often caches user prompts and model responses, an attacker could gain access to entire conversation histories or proprietary data used to fine-tune the AI. The SSRF vulnerability compounds this risk by allowing attackers to probe internal network services and access cloud metadata services (such as AWS IMDS), which can facilitate the theft of temporary IAM role credentials and lead to a full compromise of the hosting cloud account.
Suggested Corrections:
The primary mitigation for these vulnerabilities is to upgrade Chainlit to version 2.9.4 or later, which contains the necessary security patches released on December 24, 2025. Network defenders should immediately audit their environments for any instances of Chainlit and ensure they are not running versions prior to 2.9.4.
In scenarios where patching cannot be performed immediately, it is recommended to implement Web Application Firewall (WAF) rules to monitor and block suspicious activity directed at the /project/element and /project/file/ endpoints, specifically looking for path traversal sequences (e.g., ../) or unauthorized external URLs.
Additionally, defenders should apply the principle of least privilege to the service accounts running AI applications, ensuring the process has no access to sensitive system files or internal management APIs that are not strictly necessary for its operation.
Link(s):
https://www.vulncheck.com/advisories/chainlit-arbitrary-file-read-via-project-element