Red Hat Enterprise Linux AI (RHEL AI) 3 vulnerabilities
94 known vulnerabilities in Red Hat Enterprise Linux AI (RHEL AI) 3 from Red Hat. 93 are rated high or critical, and 1 has been exploited in real attacks. Each entry lists the affected versions and the release that fixes it, where the vendor has published one.
Latest first
- 7.5 High severity
Pillow: Out-of-bounds Write
Pillow is a Python imaging library. From 10.3.0 to before 12.1.1, an out-of-bounds write may be triggered when loading a specially crafted PSD image. This vulnerability is fixed in 12.1.1.
- 7.6 High severity
Red Hat OpenShift AI 3.3: Improper Control of Generation of Code
DiskCache (python-diskcache) through 5.6.3 uses Python pickle for serialization by default. An attacker with write access to the cache directory can achieve arbitrary code execution when a victim application reads from the cache.
- 7.5 High severity
axios: Improper Check for Unusual or Exceptional Conditions
Axios is a promise based HTTP client for the browser and Node.js. Prior to versions 0.30.3 and 1.13.5, the mergeConfig function in axios crashes with a TypeError when processing configuration objects containing __proto__ as an own property. An attacker can tri
- 9.8 Critical severity
jsonpath: Improper Control of Generation of Code
Versions of the package jsonpath before 1.3.0 are vulnerable to Arbitrary Code Injection via unsafe evaluation of user-supplied JSON Path expressions. The library relies on the static-eval module to process JSON Path input, which is not designed to handle untr
- 8.6 High severity
pydantic-ai: Server-Side Request Forgery
Pydantic AI is a Python agent framework for building applications and workflows with Generative AI. From 0.0.26 to before 1.56.0, aServer-Side Request Forgery (SSRF) vulnerability exists in Pydantic AI's URL download functionality. When applications accept mes
- 7.5 High severity
fastify: Interpretation Conflict
Fastify is a fast and low overhead web framework, for Node.js. Prior to version 5.7.2, a validation bypass vulnerability exists in Fastify where request body validation schemas specified by Content-Type can be completely circumvented. By appending a tab charac
- 9.8 Critical severity
vllm: Insertion of Sensitive Information into Log File
vLLM is an inference and serving engine for large language models (LLMs). From 0.8.3 to before 0.14.1, when an invalid image is sent to vLLM's multimodal endpoint, PIL throws an error. vLLM returns this error to the client, leaking a heap address. With this le
- 7.1 High severity
vllm: Server-Side Request Forgery
vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and l
- 7.5 High severity
python-multipart: Improper Limitation of a Pathname to a Restricted Directory
Python-Multipart is a streaming multipart parser for Python. Prior to version 0.0.22, a Path Traversal vulnerability exists when using non-default configuration options `UPLOAD_DIR` and `UPLOAD_KEEP_FILENAME=True`. An attacker can write uploaded files to arbit
- 7.0 High severity
cli: Incorrect Permission Assignment for Critical Resource
npm cli Incorrect Permission Assignment Local Privilege Escalation Vulnerability. This vulnerability allows local attackers to escalate privileges on affected installations of npm cli. An attacker must first obtain the ability to execute low-privileged code on
- 7.8 High severity
Sentencepiece: Improper Restriction of Operations within the Bounds of a Memory Buffer
Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure.
- 9.8 Critical severity
vllm: Improper Control of Generation of Code
vLLM is an inference and serving engine for large language models (LLMs). Starting in version 0.10.1 and prior to version 0.14.0, vLLM loads Hugging Face `auto_map` dynamic modules during model resolution without gating on `trust_remote_code`, allowing attacke
- 7.8 High severity
Fence Agents Remediation Operator: Deserialization of Untrusted Data
An undocumented and unsafe feature in the PLY (Python Lex-Yacc) library 3.11 allows Remote Code Execution (RCE) via the `picklefile` parameter in the `yacc()` function. This parameter accepts a `.pkl` file that is deserialized with `pickle.load()` without vali
- 7.5 High severity
aiohttp: Improper Handling of Highly Compressed Data (Data Amplification)
AIOHTTP is an asynchronous HTTP client/server framework for asyncio and Python. Versions 3.13.2 and below allow a zip bomb to be used to execute a DoS against the AIOHTTP server. An attacker may be able to send a compressed request that when decompressed by AI