CVE-2024-9070: bentoml/bentoml · Deserialization of Untrusted Data
Affects bentoml/bentomlSource CVE Database V5
Not known to be exploited · CVSS 9.8 Critical.
Data as of 26 Oct 2026, 17:16 UTC
- EPSS
- 0.9% chance of exploitation in 30 days – higher than 60% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:HExploitation evidence
Disclosed: evidence found.
CVE published 20 Mar 2025
Detectable: no evidence on record.
No public nuclei template indexed
Weaponised: no evidence on record.
No public exploit code indexed in Metasploit or Exploit-DB
Exploited in the wildExploited: no evidence on record.
Not in CISA KEV and no source reports exploitation
Observed by OffSeq MirageSeen by Mirage: no evidence on record.
No attack traffic recorded by OffSeq Mirage sensors
A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 26 Oct 2026.
What to do
Affected software
| Product | Affected | Fixed in |
|---|---|---|
PyPIbentomlpkg:pypi/bentomlOther threats | Affected– | Fixed in– |
Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.
AI-suggested
- Upgrade BentoML to the latest version once an official patch addressing CVE-2024-9070 is released. Monitor BentoML’s official channels for updates.
- Until a patch is available, restrict network access to the BentoML runner server by implementing strict firewall rules and network segmentation to limit exposure to untrusted networks.
- Implement input validation and sanitization on all parameters, especially args-number, to prevent unsafe deserialization triggers.
- Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions to detect anomalous behaviors indicative of deserialization attacks.
- Conduct thorough code reviews and penetration testing focusing on deserialization processes within BentoML deployments.
- Use containerization or sandboxing to isolate BentoML components, minimizing the impact of potential exploitation.
- Maintain up-to-date backups and incident response plans tailored to AI/ML infrastructure compromises.
- Educate development and operations teams about the risks of unsafe deserialization and secure coding practices.
Description
A deserialization vulnerability exists in BentoML's runner server in bentoml/bentoml versions <=1.3.4.post1. By setting specific parameters, an attacker can execute unauthorized arbitrary code on the server, causing severe harm. The vulnerability is triggered when the args-number parameter is greater than 1, leading to automatic deserialization and arbitrary code execution.