Cyber Resilience

CVE-2025-27520

CriticalPublic PoCRCE

Published: 04 April 2025

Published
04 April 2025
Modified
27 June 2025
KEV Added
Patch
CVSS Score v3.1 9.8 CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H
EPSS Score 0.7576 98.9th percentile
Risk Priority 65 60% EPSS · 20% KEV · 20% CVSS

Summary

CVE-2025-27520 is a critical-severity Deserialization of Untrusted Data (CWE-502) vulnerability in Bentoml Bentoml. Its CVSS base score is 9.8 (Critical).

Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 1.1% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.

This vulnerability is AI-related — categorised as NLP and Transformers; in the Supply Chain and Deployment risk domain.

Deeper analysis

BentoML is a Python library for building online serving systems optimized for AI applications and model inference. CVE-2025-27520 is a remote code execution vulnerability caused by insecure deserialization in version 1.4.2, residing in an unsafe code segment within serde.py. The flaw carries a CVSS score of 9.8 and is categorized under CWE-502.

Any unauthenticated remote attacker can exploit the issue over the network to execute arbitrary code on the server with no user interaction required, achieving full confidentiality, integrity, and availability impact.

The vulnerability is fixed in release 1.4.3, as noted in the project's GitHub security advisory GHSA-33xw-247w-6hmc and the associated commit that addresses the deserialization logic.

BentoML's focus on AI model serving makes the flaw particularly relevant to machine-learning infrastructure. The associated EPSS score reached a peak of 0.8735 with a current value of 0.7576, indicating notable post-disclosure exploitation interest.

EU & UK References

Vulnerability details

BentoML is a Python library for building online serving systems optimized for AI apps and model inference. A Remote Code Execution (RCE) vulnerability caused by insecure deserialization has been identified in the latest version (v1.4.2) of BentoML. It allows any…

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unauthenticated user to execute arbitrary code on the server. It exists an unsafe code segment in serde.py. This vulnerability is fixed in 1.4.3.

CWE(s)

AI Security AnalysisAI

AI Category
NLP and Transformers
Risk Domain
Supply Chain and Deployment
OWASP Top 10 for LLMs 2025
None mapped
Classification Reason
Matched keywords: ai, bentoml

Related Threats

MITRE ATT&CK Enterprise TechniquesAI

T1190 Exploit Public-Facing Application Initial Access
Adversaries may attempt to exploit a weakness in an Internet-facing host or system to initially access a network.
Why these techniques?

The insecure deserialization vulnerability (CWE-502) in BentoML's HTTP API endpoints (e.g., /summarize) enables unauthenticated remote code execution via malicious pickle payloads, directly mapping to exploitation of public-facing applications.

Affected Assets

bentoml
bentoml
1.3.4 — 1.4.2

Mitigating Controls

Likely Mitigating Controls AI

Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.

addresses: CWE-502

Penetration testing supplies malicious serialized objects, detecting unsafe deserialization and supporting corrective actions.

addresses: CWE-502

Evaluation of untrusted data handling (deserialization testing) reveals unsafe processing, which the required remediation process addresses.

addresses: CWE-502

Untrusted serialized data can be deserialized and observed inside the chamber, blocking gadget-chain exploitation outside the sandbox.

addresses: CWE-502

Validates or rejects untrusted serialized data before deserialization occurs.

addresses: CWE-502

Identifies and blocks malicious code introduced through deserialization of untrusted data at system boundaries.

addresses: CWE-502

Integrity verification of serialized information can detect tampering before deserialization occurs.

addresses: CWE-502

Provenance of associated data allows detection of untrusted sources before deserialization or processing occurs.

References