Bentoml
Monthly
Arbitrary file write in BentoML prior to version 1.4.36 allows local attackers to write files to arbitrary locations on the host system by crafting malicious tar archives containing symlinks that point outside the extraction directory. The vulnerability exists because the safe_extract_tarfile() function fails to validate symlink targets, only validating the symlink path itself, enabling attackers to bypass directory traversal protections. Public exploit code exists for this vulnerability; users should upgrade to version 1.4.36 or later.
BentoML versions prior to 1.4.34 allow path traversal attacks through improperly validated file path fields in bentofile.yaml configurations, enabling attackers to embed arbitrary files from the victim's system into bento archives during the build process. This vulnerability can be exploited to exfiltrate sensitive data such as credentials, SSH keys, and environment variables into supply chain artifacts that may be pushed to registries or deployed in production environments. A patch is available in version 1.4.34.
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and EPSS exploitation probability 67.3%.
BentoML version 1.4.2 and earlier contains an unauthenticated remote code execution vulnerability through insecure deserialization. The serving endpoint accepts pickled Python objects that are deserialized without validation, allowing attackers to execute arbitrary code on any BentoML inference server.
Server-Side Request Forgery in BentoML versions 1.4.19 through 1.4.39 exposes internal CGNAT infrastructure by failing to block RFC 6598 shared address space (100.64.0.0/10) in the `make_safe_connect` safeguard. Unauthenticated remote attackers can supply crafted URLs via multipart file uploads or JSON request bodies, causing the BentoML server to make outbound requests to CGNAT-addressed internal hosts - a class of targets increasingly common in cloud-native and ISP environments. This is an incomplete fix for CVE-2025-54381; no public exploit has been identified at time of analysis, but the known patch gap and zero-prerequisite attack surface warrant prompt remediation.
Command injection in bentoml OpenLLM 0.6.30 allows local low-privilege users to execute arbitrary shell commands by supplying crafted model repository directory name arguments to the `async_run_command` function in `src/openllm/common.py`. A public proof-of-concept exploit is available per the CVSS 4.0 E:P modifier and disclosure notes, though no CISA KEV listing exists and the project had not responded to responsible disclosure at the time of reporting. The CVSS 4.0 base score of 1.9 reflects a constrained real-world risk profile due to the strictly local attack vector and limited Low-rated confidentiality, integrity, and availability impact.
Arbitrary file write in BentoML prior to version 1.4.36 allows local attackers to write files to arbitrary locations on the host system by crafting malicious tar archives containing symlinks that point outside the extraction directory. The vulnerability exists because the safe_extract_tarfile() function fails to validate symlink targets, only validating the symlink path itself, enabling attackers to bypass directory traversal protections. Public exploit code exists for this vulnerability; users should upgrade to version 1.4.36 or later.
BentoML versions prior to 1.4.34 allow path traversal attacks through improperly validated file path fields in bentofile.yaml configurations, enabling attackers to embed arbitrary files from the victim's system into bento archives during the build process. This vulnerability can be exploited to exfiltrate sensitive data such as credentials, SSH keys, and environment variables into supply chain artifacts that may be pushed to registries or deployed in production environments. A patch is available in version 1.4.34.
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Rated critical severity (CVSS 9.8), this vulnerability is remotely exploitable, no authentication required, low attack complexity. Public exploit code available and EPSS exploitation probability 67.3%.
BentoML version 1.4.2 and earlier contains an unauthenticated remote code execution vulnerability through insecure deserialization. The serving endpoint accepts pickled Python objects that are deserialized without validation, allowing attackers to execute arbitrary code on any BentoML inference server.
Server-Side Request Forgery in BentoML versions 1.4.19 through 1.4.39 exposes internal CGNAT infrastructure by failing to block RFC 6598 shared address space (100.64.0.0/10) in the `make_safe_connect` safeguard. Unauthenticated remote attackers can supply crafted URLs via multipart file uploads or JSON request bodies, causing the BentoML server to make outbound requests to CGNAT-addressed internal hosts - a class of targets increasingly common in cloud-native and ISP environments. This is an incomplete fix for CVE-2025-54381; no public exploit has been identified at time of analysis, but the known patch gap and zero-prerequisite attack surface warrant prompt remediation.
Command injection in bentoml OpenLLM 0.6.30 allows local low-privilege users to execute arbitrary shell commands by supplying crafted model repository directory name arguments to the `async_run_command` function in `src/openllm/common.py`. A public proof-of-concept exploit is available per the CVSS 4.0 E:P modifier and disclosure notes, though no CISA KEV listing exists and the project had not responded to responsible disclosure at the time of reporting. The CVSS 4.0 base score of 1.9 reflects a constrained real-world risk profile due to the strictly local attack vector and limited Low-rated confidentiality, integrity, and availability impact.