bentoml OpenLLM Model Repository Directory Name common.py async_run_command command injection
A vulnerability was found in bentoml OpenLLM 0.6.30. This affects the function async_run_command of the file src/openllm/common.py of the component Model Repository Directory Name Handler. Performing a manipulation of the argument cmd results in command injection. Attacking locally is a requirement. The exploit has been made public and could be used. The project was informed of the problem early through an issue report but has not responded yet.
BentoML: Dockerfile command injection via docker.base_image
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.39, src/bentoml/_internal/container/frontend/dockerfile/templates/base_v2.j2 interpolates docker.base_image raw with no escaping, newline filtering, or validation. A malicious bento.yaml with a multi-line docker.base_image value smuggles arbitrary Dockerfile directives into the generated Dockerfile, and bentoml containerize then runs docker build which executes the injected RUN directives on the victim host. This vulnerability is fixed in 1.4.39.
BentoML: Dockerfile command injection via envs[*].name in bentofile.yaml
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.39, a malicious bentofile.yaml containing a newline-injected value in envs[*].name produces unquoted RUN directives in the BentoML-generated Dockerfile. When the victim runs bentoml containerize on the imported bento, those RUN directives execute on the host during docker build. This vulnerability is fixed in 1.4.39.
BentoML has Information Disclosure in `bentoml build` via symlink traversal in the build context
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.38 and prior, the build packaging workflow follows attacker-controlled symlinks inside the build context and copies the referenced file contents into the generated Bento artifact. If a victim builds an untrusted repository or other attacker-supplied build context, the attacker can place a symlink such as loot.txt -> /tmp/outside-marker.txt or a link to a more sensitive local file. When bentoml build runs, BentoML dereferences the symlink and packages the target file contents into the Bento. The leaked file can then propag
BentoML has a Server-Side Template Injection via unsandboxed Jinja2 Environment in Dockerfile generation
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.38, the Dockerfile generation function generate_containerfile() in src/bentoml/_internal/container/generate.py uses an unsandboxed jinja2.Environment with the jinja2.ext.do extension to render user-provided dockerfile_template files. When a victim imports a malicious bento archive and runs bentoml containerize, attacker-controlled Jinja2 template code executes arbitrary Python directly on the host machine, bypassing all container isolation. This vulnerability is fixed in 1.4.38.
BentoML: command injection in cloud deployment setup script (deployment.py)
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.38, the cloud deployment path in src/bentoml/_internal/cloud/deployment.py was not included in the fix for CVE-2026-33744. Line 1648 interpolates system_packages directly into a shell command using an f-string without any quoting. The generated script is uploaded to BentoCloud as setup.sh and executed on the cloud build infrastructure during deployment, making this a remote code execution on the CI/CD tier. This vulnerability is fixed in 1.4.38.
BentoML has Dockerfile Command Injection via system_packages in bentofile.yaml
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.37, the `docker.system_packages` field in `bentofile.yaml` accepts arbitrary strings that are interpolated directly into Dockerfile `RUN` commands without sanitization. Since `system_packages` is semantically a list of OS package names (data), users do not expect values to be interpreted as shell commands. A malicious `bentofile.yaml` achieves arbitrary command execution during `bentoml containerize` / `docker build`. Version 1.4.37 fixes the issue.
BentoML has an Arbitrary File Write via Symlink Path Traversal in Tar Extraction
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.36, the safe_extract_tarfile() function validates that each tar member's path is within the destination directory, but for symlink members it only validates the symlink's own path, not the symlink's target. An attacker can create a malicious bento/model tar file containing a symlink pointing outside the extraction directory, followed by a regular file that writes through the symlink, achieving arbitrary file write on the host filesystem. This vulnerability is fixed in 1.4.36.
BentoML has a Path Traversal via Bentofile Configuration
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to version 1.4.34, BentoML's `bentofile.yaml` configuration allows path traversal attacks through multiple file path fields (`description`, `docker.setup_script`, `docker.dockerfile_template`, `conda.environment_yml`). An attacker can craft a malicious bentofile that, when built by a victim, exfiltrates arbitrary files from the filesystem into the bento archive. This enables supply chain attacks where sensitive files (SSH keys, credentials, environment variables) are silently embedded in bentos and exposed when pushed to registries
BentoML is Vulnerable to an SSRF Attack Through File Upload Processing
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. In versions 1.4.0 until 1.4.19, the file upload processing system contains an SSRF vulnerability that allows unauthenticated remote attackers to force the server to make arbitrary HTTP requests. The vulnerability stems from the multipart form data and JSON request handlers, which automatically download files from user-provided URLs without validating whether those URLs point to internal network addresses, cloud metadata endpoints, or other restricted resources. The documentation explicitly promotes this URL-based file upload feature, mak
Insecure Deserialization leads to RCE in BentoML's runner server
BentoML is a Python library for building online serving systems optimized for AI apps and model inference. Prior to 1.4.8, there was an insecure deserialization in BentoML's runner server. By setting specific headers and parameters in the POST request, it is possible to execute any unauthorized arbitrary code on the server, which will grant the attackers to have the initial access and information disclosure on the server. This vulnerability is fixed in 1.4.8.
BentoML Allows Remote Code Execution (RCE) via Insecure Deserialization
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 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.
BentoML vulnerable to Uncontrolled Resource Consumption
In bentoml/bentoml version 1.3.9, the `/login` endpoint of the newly integrated Gradio app is vulnerable to a Denial of Service (DoS) attack. This vulnerability can be exploited by appending characters, such as dashes (-), to the end of a multipart boundary in an HTTP request. The server continuously processes each character, leading to excessive resource consumption and rendering the service unavailable. The issue is unauthenticated and does not require any user interaction.
An open redirect vulnerability in bentoml/bentoml v1.3.9 allows a remote unauthenticated attacker to redirect users to arbitrary websites via a specially crafted URL. This can be exploited for phishing attacks, malware distribution, and credential theft.
Deserialization Vulnerability in BentoML's Runner Server in bentoml/bentoml
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.
BentoML version v1.3.4post1 is vulnerable to a Denial of Service (DoS) attack. The vulnerability can be exploited by appending characters, such as dashes (-), to the end of a multipart boundary in an HTTP request. This causes the server to continuously process each character, leading to excessive resource consumption and rendering the service unavailable. The issue is unauthenticated and does not require any user interaction, impacting all users of the service.
Insecure Deserialization Leading to RCE in bentoml/bentoml
An insecure deserialization vulnerability exists in the BentoML framework, allowing remote code execution (RCE) by sending a specially crafted POST request. By exploiting this vulnerability, attackers can execute arbitrary commands on the server hosting the BentoML application. The vulnerability is triggered when a serialized object, crafted to execute OS commands upon deserialization, is sent to any valid BentoML endpoint. This issue poses a significant security risk, enabling attackers to compromise the server and potentially gain unauthorized access or control.