A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `safe_mode` and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the `from_config()` method.

Affected: PyPI: keras

A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `safe_mode` and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the `from_config()` method.

Affected: PyPI: keras

A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `safe_mode` and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the `from_config()` method.

bouncycastle: BC-JAVA: GOSTCTR implementation unable to process more than 255 blocks correctly vllm: HTTP header size limit not enforced allows Denial of Service from Unauthenticated requests golang: net/url: Memory exhaustion in query parameter parsing in net/url axios: Axios: Server-Side Request Forgery and proxy bypass due to improper hostname normalization aiohttp: aiohttp: Denial of Service via specially crafted POST request aiohttp: aiohttp: Denial of Service via memory exhaustion from crafted POST request keras: Keras: Arbitrary Code Execution Vulnerability Bypassing Safe Mode lodash: lodash: Arbitrary code execution via untrusted input in template imports fast-uri: fast-uri: Path traversal vulnerability allows bypass of security policies pyasn1: pyasn1: Denial of Service due to memory exhaustion from malformed RELATIVE-OID pytorch: PyTorch: Arbitrary code execution via malicious checkpoint file loading xgrammar: xgrammar: Denial of Service via multi-level nested syntax vLLM: vLLM: Server-Side Request Forgery bypass via inconsistent URL parsing onnx: ONNX: Information Disclosure via Path Traversal Vulnerability vllm: vLLM: Remote code execution due to hardcoded trust_remote_code setting onnx: ONNX: Untrusted Model Repository Warnings Suppressed python-dotenv: python-dotenv: Arbitrary file overwrite via symbolic link following immutable-js: Immutable.js: Arbitrary code execution via Prototype Pollution svgo: SVGO: Denial of Service via XML entity expansion tornado-pyth…

Affected: Ubuntu:18.04:LTS: keras, Ubuntu:20.04:LTS: keras

A vulnerability in the `TFSMLayer` class of the `keras` package, version 3.13.0, allows attacker-controlled TensorFlow SavedModels to be loaded during deserialization of `.keras` models, even when `safe_mode=True`. This bypasses the security guarantees of `safe_mode` and enables arbitrary attacker-controlled code execution during model inference under the victim's privileges. The issue arises due to the unconditional loading of external SavedModels, serialization of attacker-controlled file paths, and the lack of validation in the `from_config()` method.