High-Severity Vulnerabilities in Chainlit Expose Enterprises to Data Breaches
Cybersecurity firm Zafran has disclosed two high-severity vulnerabilities in Chainlit, an open-source Python framework for building conversational AI applications, which could allow attackers to access sensitive data from exposed servers. With over 700,000 monthly downloads on PyPI, Chainlit integrates with major AI platforms like LangChain, OpenAI, Bedrock, and Llama, making it a widely used tool in enterprise and academic environments.
The flaws, tracked as CVE-2026-22218 and CVE-2026-22219, affect Chainlit versions prior to 2.9.4 and enable attackers to:
- Read arbitrary files on the server, including environment variables containing API keys, credentials, internal IPs, and cloud metadata.
- Forge authentication tokens by exploiting the
CHAINLIT_AUTH_SECRETvariable, potentially leading to account takeovers. - Leak database contents, including user conversations, messages, and metadata, if the deployment uses SQLAlchemy with SQLite.
- Exfiltrate LangChain cache data, exposing prompts and responses from all users.
- Retrieve application source code from the Chainlit directory.
For deployments on AWS, the vulnerabilities could allow attackers to access IAM tokens and role endpoints, enabling lateral movement within the cloud environment. Once cloud credentials are compromised, attackers may gain access to storage buckets, secret managers, LLMs, and other internal resources.
Zafran identified multiple publicly accessible Chainlit servers belonging to large enterprises and academic institutions, highlighting the risk of widespread exposure. The vulnerabilities underscore the potential for sensitive data disclosure and cloud-based lateral movement in AI-driven applications.
Source: https://www.securityweek.com/chainlit-vulnerabilities-may-leak-sensitive-information/
Zafran TPRM report: https://www.rankiteo.com/company/zafran-security
"id": "zaf1768921979",
"linkid": "zafran-security",
"type": "Vulnerability",
"date": "1/2026",
"severity": "",
"impact": "1",
"explanation": "Attack without any consequences"{'affected_entities': [{'customers_affected': 'Enterprises and academic '
'institutions using Chainlit '
'versions prior to 2.9.4',
'industry': 'Technology/AI',
'name': 'Chainlit',
'size': '700,000+ monthly downloads',
'type': 'Open-source software framework'}],
'attack_vector': 'Exploitation of software vulnerabilities',
'data_breach': {'data_exfiltration': 'Possible (via arbitrary file reads and '
'database leaks)',
'file_types_exposed': ['Environment variables',
'SQLite databases',
'LangChain cache data',
'Application source code'],
'personally_identifiable_information': 'Potential (user '
'conversations and '
'metadata)',
'sensitivity_of_data': 'High (includes authentication '
'secrets, cloud credentials, and '
'personally identifiable information)',
'type_of_data_compromised': ['API keys',
'credentials',
'internal IPs',
'cloud metadata',
'user conversations',
'messages',
'metadata',
'prompts and responses',
'application source code',
'IAM tokens']},
'description': 'Cybersecurity firm Zafran has disclosed two high-severity '
'vulnerabilities in Chainlit, an open-source Python framework '
'for building conversational AI applications, which could '
'allow attackers to access sensitive data from exposed '
'servers. The flaws enable attackers to read arbitrary files, '
'forge authentication tokens, leak database contents, '
'exfiltrate LangChain cache data, and retrieve application '
'source code. For AWS deployments, the vulnerabilities could '
'lead to IAM token access and lateral movement within cloud '
'environments.',
'impact': {'brand_reputation_impact': 'Potential reputational damage due to '
'sensitive data exposure',
'data_compromised': ['API keys',
'credentials',
'internal IPs',
'cloud metadata',
'user conversations',
'messages',
'metadata',
'prompts and responses',
'application source code',
'IAM tokens'],
'identity_theft_risk': 'High (due to potential account takeovers '
'and sensitive data exposure)',
'operational_impact': 'Potential lateral movement within cloud '
'environments and access to internal '
'resources',
'systems_affected': ['Chainlit servers', 'AWS cloud environments']},
'post_incident_analysis': {'corrective_actions': 'Patch management, '
'credential rotation, and '
'cloud environment audits',
'root_causes': 'High-severity vulnerabilities in '
'Chainlit (CVE-2026-22218 and '
'CVE-2026-22219) allowing arbitrary '
'file reads and authentication '
'token forgery'},
'recommendations': 'Update Chainlit to version 2.9.4 or later, audit exposed '
'servers, rotate compromised credentials, and implement '
'enhanced monitoring for cloud environments.',
'references': [{'source': 'Zafran'}],
'response': {'remediation_measures': 'Update to Chainlit version 2.9.4 or '
'later',
'third_party_assistance': 'Zafran (cybersecurity firm)'},
'title': 'High-Severity Vulnerabilities in Chainlit Expose Enterprises to '
'Data Breaches',
'type': 'Data Breach',
'vulnerability_exploited': ['CVE-2026-22218', 'CVE-2026-22219']}