Mardi 1 septembre 2026

scrapy

Scrapy is a high-level web crawling and scraping framework for Python. Prior to 2.17.0, in scrapy/core/downloader/handlers/s3.py, Scrapy's S3DownloadHandler converts an S3-scheme bucket and key request into a plaintext HTTP request to the corresponding S3 endpoint unless request.meta["is_secure"] is explicitly enabled, then signs and sends the plaintext request with configured AWS credentials. A network attacker who can observe traffic between Scrapy and S3 can read the bucket and key path, AWS Authorization header, X-Amz-Security-Token when temporary credentials are used, S3 object contents, and S3 response headers. An active man-in-the-middle attacker can also modify the plaintext S3 response body, status code, and headers before Scrapy processes them, causing scraped-data poisoning, poisoned exports, HTTP cache poisoning when caching is enabled, or influence over later crawl targets through forged redirects or attacker-controlled links. Users making S3-scheme requests with AWS credentials are affected. This issue is fixed in version 2.17.0.

AWS sagemaker-python-sdk

Cleartext storage of sensitive information in the @step and @remote decorator pipeline component in Amazon SageMaker Python SDK before v3.11.0 and v2.256.0 might allow an authenticated remote user to extract the HMAC signing key from SageMaker DescribePipeline API responses and forge valid integrity signatures for specially crafted function payloads, achieving code execution in another user's pipeline execution context within the same AWS account.

An eval() injection vulnerability in the get_list function in modules/meta_parser.py in lllyasviel Fooocus 2.1.854 through 2.5.5 allows remote attackers to execute arbitrary Python code via a crafted styles payload in the EXIF metadata of an uploaded image file.

modelscope

ModelScope uses PyYAML's unsafe yaml.Loader to parse model configuration files, allowing arbitrary code execution through Python object construction tags. Attackers can craft malicious model repositories with poisoned configuration files that execute code when loaded by users.

Hugging Face Transformers

A vulnerability in Hugging Face Transformers (versions 4.57.0 to 5.16.1) allows remote Python files to be written to local disk without user consent when using GenerativePreTrainedModel.load_custom_generate(). The function fetches and caches a remote module file before performing the required trust_remote_code consent check, inverting the security model enforced by other code-loading paths (such as AutoConfig, AutoModel, and AutoTokenizer). As a result, attacker‑controlled Python code from custom_generate/generate.py is copied into the user’s ~/.cache/huggingface/modules directory even if the user declines the trust prompt. Although execution is correctly gated, the file write is not reversible and can persist across sessions. This can lead to persistent, unauthorized files on disk and stale cache collisions where cached attacker code may later be executed during trusted model loads. The issue stems from an unconditional file write in dynamic_module_utils.py prior to any trust verification.

tornadoweb tornado

Tornado is a Python web framework and asynchronous networking library. Prior to 6.5.8, Tornado parses application/x-www-form-urlencoded request bodies with urllib.parse.parse_qs in tornado/escape.py without passing max_num_fields. RequestHandler._execute in tornado/web.py parses the body before handler dispatch through HTTPServerRequest._parse_body and parse_body_arguments in tornado/httputil.py, so an unauthenticated request body containing millions of separator-delimited fields can synchronously stall the single-threaded event loop and delay every connection. The body is bounded only by max_buffer_size, which defaults to 104857600 bytes. This issue is fixed in version 6.5.8.