CVE-2026-27740 in Discourse
Summary
by MITRE • 03/19/2026
Discourse is an open-source discussion platform. Versions prior to 2026.3.0-latest.1, 2026.2.1, and 2026.1.2 have a cross-site scripting vulnerability that arises because the system trusts the raw output from an AI Large Language Model (LLM) and renders it using htmlSafe in the Review Queue interface without adequate sanitization. A malicious attacker can use valid Prompt Injection techniques to force the AI to return a malicious payload (e.g., tags). When a Staff member (Admin/Moderator) views the flagged post in the Review Queue, the payload executes. Versions 2026.3.0-latest.1, 2026.2.1, and 2026.1.2 contain a patch. As a workaround, temporarily disable AI triage automation scripts.
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Analysis
by VulDB Data Team • 03/25/2026
This cross-site scripting vulnerability in Discourse affects versions prior to 2026.3.0-latest.1, 2026.2.1, and 2026.1.2, creating a critical security risk through improper input handling in the Review Queue interface. The flaw stems from the platform's trust in raw AI output without adequate sanitization before rendering, specifically when using the htmlSafe function to display content from AI Large Language Model responses. This vulnerability represents a classic case of insecure data handling where automated security controls fail to properly validate and sanitize untrusted data sources.
The technical exploitation occurs through prompt injection techniques that manipulate the AI system to generate malicious payloads containing HTML tags or JavaScript code. When staff members with administrative or moderator privileges view flagged posts in the Review Queue, the malicious code executes in their browser context, potentially allowing attackers to perform actions with elevated privileges. This vulnerability maps directly to CWE-79 - Improper Neutralization of Input During Web Page Generation, specifically manifesting as a client-side cross-site scripting attack where the malicious input originates from a trusted source within the application's own infrastructure.
The operational impact of this vulnerability extends beyond simple script execution, as it provides attackers with a potential foothold for more sophisticated attacks including session hijacking, privilege escalation, and data exfiltration. Staff members who regularly access the Review Queue become prime targets for exploitation, making this vulnerability particularly dangerous in environments where multiple administrators and moderators interact with flagged content. The risk is amplified by the fact that the vulnerability operates silently without requiring user interaction beyond viewing the malicious content, and the AI automation makes it difficult to predict or prevent malicious prompt injection attempts.
Security mitigations for this vulnerability include upgrading to the patched versions 2026.3.0-latest.1, 2026.2.1, or 2026.1.2, which implement proper sanitization of AI-generated content before rendering. The recommended workaround of temporarily disabling AI triage automation scripts provides an effective temporary solution while maintaining platform functionality. Organizations should also implement comprehensive input validation policies for all AI-generated content, establish automated content sanitization processes, and consider implementing additional security controls such as content security policies to limit the execution of malicious scripts. This vulnerability demonstrates the critical importance of treating AI-generated content as untrusted input and implementing proper security controls throughout the entire data processing pipeline. The attack vector aligns with ATT&CK technique T1059.007 - Command and Scripting Interpreter: JavaScript, and T1566.002 - Phishing: Spearphishing via Service, highlighting the intersection of AI security risks with traditional web application vulnerabilities.