CVE-2024-9056 is a vulnerability classified under CWE-770, which concerns the allocation of resources without limits or throttling. Specifically, in BentoML version v1.3.4post1, the multipart boundary parsing logic in HTTP requests is flawed. An attacker can append additional characters, such as dashes (-), to the end of a multipart boundary string. This malformed boundary causes the server to enter a loop or continuous processing state, consuming excessive CPU and memory resources. Because the server does not impose limits or throttling on this processing, the resource consumption grows until the service becomes unresponsive, resulting in a Denial of Service (DoS). The vulnerability is exploitable remotely over the network without any authentication or user interaction, increasing its risk profile. The affected product, BentoML, is a popular open-source framework for serving machine learning models, widely used in AI/ML deployments. The vulnerability does not impact confidentiality or integrity but severely impacts availability. No patches or official fixes are linked yet, and no exploits have been reported in the wild as of the publication date. The CVSS v3.0 score of 7.5 (AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H) reflects the high impact on availability with low attack complexity and no privileges or interaction required.

For European organizations, the primary impact of CVE-2024-9056 is service disruption due to Denial of Service attacks targeting BentoML-based AI/ML model serving platforms. This can lead to downtime of critical AI services, affecting business operations, customer-facing applications, and internal decision-making processes that rely on real-time AI inference. Industries such as finance, healthcare, manufacturing, and telecommunications, which increasingly depend on AI models served via BentoML, may experience operational delays and loss of productivity. Additionally, prolonged outages could damage organizational reputation and trust. Since the vulnerability requires no authentication, attackers can launch DoS attacks from anywhere, increasing the threat surface. The lack of known exploits in the wild currently limits immediate risk, but the ease of exploitation and high impact on availability make this a significant concern for European entities deploying BentoML in production environments.

1. Immediately monitor and restrict incoming HTTP requests with malformed multipart boundaries, especially those containing unusual trailing characters like dashes. 2. Implement rate limiting and resource throttling on multipart/form-data parsing components to prevent excessive resource consumption. 3. Deploy Web Application Firewalls (WAFs) with custom rules to detect and block suspicious multipart boundary patterns. 4. Upgrade BentoML to a patched version once available; in the meantime, consider applying temporary patches or workarounds such as input validation filters. 5. Isolate BentoML serving infrastructure behind reverse proxies that can filter malformed HTTP requests. 6. Conduct regular stress testing and monitoring to detect abnormal resource usage patterns indicative of exploitation attempts. 7. Educate DevOps and security teams about this vulnerability to ensure rapid detection and response. 8. Engage with BentoML community or vendor for updates and official patches.