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VulnerabilityPublished Updated

CVE-2024-9070: bentoml/bentoml · Deserialization of Untrusted Data

Affects bentoml/bentomlSource CVE Database V5

0

Not known to be exploited · CVSS 9.8 Critical.

Data as of 26 Oct 2026, 14:05 UTC

CVSS 3.0 base score
9.8Critical
How severe the flaw is on the 0–10 CVSS scale.
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:H

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 20 Mar 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq 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

ProductAffectedFixed in
PyPIbentomlpkg:pypi/bentomlOther threatsAffected–Fixed in–

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

  1. Upgrade BentoML to the latest version once an official patch addressing CVE-2024-9070 is released. Monitor BentoML’s official channels for updates.
  2. 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.
  3. Implement input validation and sanitization on all parameters, especially args-number, to prevent unsafe deserialization triggers.
  4. Employ runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions to detect anomalous behaviors indicative of deserialization attacks.
  5. Conduct thorough code reviews and penetration testing focusing on deserialization processes within BentoML deployments.
  6. Use containerization or sandboxing to isolate BentoML components, minimizing the impact of potential exploitation.
  7. Maintain up-to-date backups and incident response plans tailored to AI/ML infrastructure compromises.
  8. 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.

References

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