CVE-2024-8789 identifies a Regular Expression Denial of Service (ReDoS) vulnerability in the lunary-ai/lunary project, specifically in version git 105a3f6 and potentially other unspecified versions. The core issue arises because the application permits users to upload and execute their own regular expressions on the server side without adequate safeguards. Certain regular expressions, especially those with nested quantifiers or ambiguous patterns, can cause the regex engine to exhibit exponential time complexity relative to input size. When such a crafted regex is processed, it can consume excessive CPU resources, causing the server to become unresponsive or crash, effectively resulting in a denial of service. The vulnerability is remotely exploitable without requiring authentication or user interaction, increasing the attack surface. The CVSS v3.0 score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) highlights that the attack can be launched over the network with low complexity and no privileges, impacting availability only. No patches or fixes are currently linked, and no active exploitation has been reported. The vulnerability is classified under CWE-1333, which pertains to inefficient regular expression complexity leading to performance degradation. This issue is particularly critical for environments where lunary-ai/lunary is exposed to untrusted users or external inputs, such as public-facing AI or data processing services.

For European organizations, exploitation of this vulnerability could lead to significant service disruptions, especially in sectors relying on lunary-ai/lunary for AI-driven data analysis or automation. The denial of service could cause downtime, impacting business continuity, customer trust, and potentially leading to financial losses. Since the vulnerability affects availability without compromising confidentiality or integrity, the primary risk is operational. Organizations with public-facing services or multi-tenant environments are at higher risk, as attackers can remotely trigger the ReDoS condition without authentication. This could also affect compliance with European regulations like GDPR if service outages impact data processing obligations. Furthermore, sectors such as finance, healthcare, research institutions, and technology companies—where AI tools are increasingly integrated—may face amplified risks due to their reliance on uninterrupted AI services. The lack of known exploits in the wild suggests a window for proactive mitigation before widespread attacks occur.

To mitigate CVE-2024-8789, organizations should implement multiple layers of defense: 1) Restrict or disable the ability for users to upload or execute arbitrary regular expressions unless absolutely necessary. 2) If user-supplied regexes are required, enforce strict validation and complexity limits to prevent patterns with exponential runtime, such as limiting nested quantifiers and disallowing ambiguous constructs. 3) Employ regex libraries or engines that support safe regex evaluation or have built-in timeouts to abort long-running matches. 4) Monitor server resource usage and implement rate limiting or throttling on regex execution requests to detect and block abuse. 5) Isolate regex execution in sandboxed environments to prevent broader system impact. 6) Keep lunary-ai/lunary software up to date and monitor vendor advisories for patches addressing this vulnerability. 7) Conduct regular security assessments and fuzz testing on regex inputs to identify potential ReDoS patterns proactively. 8) Consider deploying Web Application Firewalls (WAFs) with custom rules to detect and block suspicious regex payloads. These targeted mitigations go beyond generic advice by focusing on controlling regex complexity and execution environment hardening.