Python: уязвимости стека · Страница 32

wger before 2.5 Uncontrolled Resource Consumption via date_sequence

wger before 2.5 fails to validate the maximum duration of routine date ranges, allowing authenticated users to create routines spanning arbitrarily long periods. Attackers can trigger the date_sequence computation via routine detail endpoints, forcing the server to iterate thousands of times per request and exhaust worker threads, denying service to legitimate users.

• Обновитесь до 2.5

Arbitrary file read via workspace confinement bypass in local-operator /v1/chat/agents/{id}/edit

The `/v1/chat/agents/{agent_id}/edit` endpoint in local-operator versions before 0.47.5 resolves the caller-supplied `file_path` with `expanduser().resolve()` and reads it without checking that it lies inside the agent's workspace. An unauthenticated client with network access to the API can supply an absolute path or a path containing traversal sequences and obtain the contents of any file readable by the server process, which are placed in the model prompt and returned in the response. Version 0.47.5 resolves server-side reads within the agent's configured workspace and rejects canonical paths outside it (including symlink and junction esc

• Обновитесь до 0.47.5

Path traversal and arbitrary directory deletion/overwrite via agent profile import in local-operator

The `/v1/agents/import` endpoint and `AgentRegistry.import_agent()` in local-operator versions before 0.47.5 trust the `id` field inside `agent.yml` of an uploaded agent profile archive when constructing the destination directory. A crafted `id` containing directory traversal sequences makes `shutil.rmtree` and `shutil.copy2` operate outside the agent registry, allowing an unauthenticated client with network access to the API to recursively delete arbitrary directories and write files with the privileges of the server process. Even without traversal, an imported archive could overwrite or delete existing local agent profiles. Version 0.47.5

• Обновитесь до 0.47.5

pymonocypher: Potential heap buffer overflow on nb_blocks in argon2i_32 when provided buffer is too small

pymonocypher uses cython to wrap the Monocypher C library. Prior to version 4.0.2.8, the argon2i_32 implementation does not check the nb_blocks size. If the caller does not provide a sufficiently large buffer based on the API contract, then argon2i_32 will write past the end of the buffer and possibly corrupt the heap. This issue has been patched in version 4.0.2.8.

• Обновитесь до 4.0.2.8

Windows ML CLI: CORS misconfig enables localhost RCE

Windows ML CLI is a command line tool for building portable, performant, and high-quality AI models for Windows ML. Prior to 0.4.0, the src/winml/modelkit/serve/cli_api.py component exposes WinML CLI commands through a localhost HTTP API without authentication and configures the allow_origins setting as a wildcard in both src/winml/modelkit/serve/cli_api.py and src/winml/modelkit/serve/app.py. A malicious website loaded by a user can send cross-origin requests to /v1/cli/build or /v1/cli/config and set the trust_remote_code parameter to true, which is converted to the --trust-remote-code command-line flag without validation. This reaches Auto

• Обновитесь до 0.4.0

HTTPX2: Streaming response decompression does not bound peak memory (decompression amplification)

HTTPX2 is a next generation HTTP client for Python. Prior to 2.12.0, the HTTPX2 content decoders in src/httpx2/httpx2/_decoders.py fully inflate each gzip, deflate, br, or zstd network chunk before iter_bytes() or aiter_bytes() yields bounded pieces to the application. A 64 KiB compressed chunk can expand to approximately 64 MiB in one intermediate allocation, so an attacker-controlled or compromised server can cause severe memory pressure or out-of-memory process termination even when the application streams the response. This issue is fixed in version 2.12.0.

• Обновитесь до 2.12.0
• Обновитесь до 2.12.0-1

HTTPX2: Secure WebSocket traffic sent without TLS through SOCKS proxies

HTTPX2 is a next generation HTTP client for Python. Prior to 2.10.0, httpcore2 fails to start TLS in src/httpcore2/httpcore2/_sync/socks_proxy.py and src/httpcore2/httpcore2/_async/socks_proxy.py when the remote origin uses wss through a SOCKS5 proxy because the TLS upgrade condition only recognizes https. HTTPX2 exposes the flaw through Client.websocket() and AsyncClient.websocket() from 2.6.0 through 2.9.1, so the opening handshake, query parameters, Authorization headers, cookies, and subsequent frames can cross the proxy path in plaintext without certificate verification. An attacker controlling or observing that path can read or modify t

• Обновитесь до 2.10.0

HTTPX2: Conflicting Content-Length and Transfer-Encoding headers can be auto-generated

HTTPX2 is a next generation HTTP client for Python. Prior to 2.11.0, Request._prepare() in src/httpx2/httpx2/_models.py can add a body-derived Content-Length header to a request that already contains a caller-supplied Transfer-Encoding header because its setdefault() processing checks each default header independently rather than treating the two framing headers as mutually exclusive. Fixed-size byte, JSON, form, and known-length multipart bodies can therefore be serialized over HTTP/1.1 with both headers, allowing request smuggling or connection desynchronization when downstream intermediaries disagree about which framing header takes preced

• Обновитесь до 2.11.0
• Обновитесь до 2.12.0-1

HTTPX2: Multipart part header injection via unvalidated file Content-Type and custom headers

HTTPX2 is a next generation HTTP client for Python. Prior to 2.11.0, FileField.render_headers() in src/httpx2/httpx2/_multipart.py directly interpolates attacker-controlled content_type values and custom headers from the files= three-element (filename, content, content_type) tuple and the files= four-element (filename, content, content_type, headers) tuple into multipart/form-data part headers without validating header names or values. CR or LF characters can terminate a part header, inject additional part headers, or end the part header block early, allowing a downstream multipart parser to treat attacker-supplied lines as genuine headers an

• Обновитесь до 2.11.0
• Обновитесь до 2.12.0-1

HTTPX2: Quadratic SSE line buffering can cause CPU denial of service

HTTPX2 is a next generation HTTP client for Python. From 2.5.0 until 2.10.0, the HTTPX2 Server-Sent Events parser in src/httpx2/httpx2/_sse.py repeatedly copies and rescans buffered text in _SSELineDecoder.decode() when an attacker-controlled or compromised SSE endpoint splits one unterminated line across many response chunks. The behavior affects httpx2.Client.sse() and httpx2.AsyncClient.sse(), and the total processing work grows quadratically with the line length, allowing a crafted stream to consume excessive CPU and block a synchronous worker or asynchronous event loop. This issue is fixed in version 2.10.0.

• Обновитесь до 2.10.0
• Обновитесь до 2.12.0-1

LiteLLM: Authenticated SSRF and provider-credential exfiltration via unvalidated request-body routing parameters

LiteLLM is a proxy server (AI Gateway) to call LLM APIs in OpenAI (or native) format. Prior to versions 1.88.6 and 1.96.2, any authenticated LiteLLM proxy user could redirect an outbound provider call to a destination the user controls and cause the proxy to send its configured provider credentials to that destination. Request validation in litellm/proxy/auth/auth_utils.py, litellm/proxy/common_request_processing.py, litellm/proxy/health_endpoints/_health_endpoints.py, litellm/proxy/image_endpoints/endpoints.py, and litellm/proxy/litellm_pre_call_utils.py used incomplete checks that did not cover every sensitive parameter or inspect equivalen

• Обновитесь до 1.88.6
• Обновитесь до 1.89.7
• Обновитесь до 1.90.7
• Обновитесь до 1.91.5
• Обновитесь до 1.92.2
• Обновитесь до 1.93.2
• Обновитесь до 1.94.3
• Обновитесь до 1.95.1
• Обновитесь до 1.96.2

Apache Spark: XSS Vulnerability in Spark Web 3.5.4

There is a lack of XSS escaping in the Spark History Server prior to 3.5.8 which allows a malicious Spark job to generate arbitrary unescaped frontend code which could lead to a minimal privilege escalation in browser. Users are encouraged to upgrade to Spark 3.5.8 or later. This CVE is marked as "low" since the path to exploit requires both relatively high permissions (ability to launch a Spark job) and requires tricking a user with higher permissions to log in and visit the Spark history web page. Users are encouraged to upgrade their Spark history servers to Spark 3.5.8 or later.

• Обновитесь до 3.5.8

Scrapy: S3DownloadHandler sends signed S3 requests over plaintext HTTP by default

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-midd

• Обновитесь до 2.17
• Обновитесь до 2.17.0
• Обновитесь до 2.17.0-1

pypdf: Possible long runtimes/large memory usage when extracting XForm objects

pypdf is a free and open-source pure-python PDF library. Prior to 6.16.1, an attacker can craft a PDF that causes pypdf/_page.py PageObject._extract_text and PageObject.extract_xform_text to traverse a directed acyclic graph of reused form XObjects in which each form invokes a child multiple times, creating exponentially many traversal paths and causing long runtimes and large memory consumption. This issue is fixed in version 6.16.1.

• Обновитесь до 6.16.1
• Обновитесь до 6.19.0-1

pypdf: Possible long runtimes/large memory usage when retrieving outlines

pypdf is a free and open-source pure-python PDF library. Prior to 6.16.1, an attacker can craft a PDF that causes pypdf/_doc_common.py _get_outline to consume long runtimes and large amounts of memory when retrieving document outlines with large numbers of entries or deeply nested reused paths because the traversal lacked global entry-count and nesting-depth limits. This issue is fixed in version 6.16.1.

• Обновитесь до 6.16.1
• Обновитесь до 6.19.0-1

pypdf: Possible infinite loop for TreeObject.insert_child

pypdf is a free and open-source pure-python PDF library. Prior to 6.16.0, an attacker can craft a PDF whose cyclic tree structure causes pypdf/generic/_data_structures.py TreeObject.insert_child to follow /Next links indefinitely when a writing code path inserts a child, producing an infinite loop. This issue is fixed in version 6.16.0.

• Обновитесь до 6.16.0
• Обновитесь до 6.19.0-1

sqlparse: Reindentation of tuple lists causes near-cap quadratic CPU consumption

sqlparse is a non-validating SQL parser module for Python. Prior to 0.6.0, sqlparse.format(sql, reindent=True) and sqlformat --reindent route attacker-controlled parenthesized tuple lists through ReindentFilter._get_offset() in sqlparse/filters/reindent.py, where _flatten_up_to_token() repeatedly rebuilds and joins the statement prefix. Thousands of offset calculations walk an expanding token tree, producing quadratic CPU consumption for inputs that remain below MAX_GROUPING_TOKENS and causing request delays, reduced throughput, or worker starvation. This issue is fixed in version 0.6.0.

• Обновитесь до 0.6.0

MLFLOW_ALLOW_PICKLE_DESERIALIZATION=False safety control bypassed by mlflow.statsmodels flavor — RCE via crafted model artifact

## Summary MLflow introduced `MLFLOW_ALLOW_PICKLE_DESERIALIZATION` as a security control to prevent unsafe `pickle.load` execution during model loading, in response to CVE-2024-37052 through CVE-2024-37060. When set to `False`, operators expect all pickle deserialization to be blocked. The most recent related fix (#21188) patched a bypass in the pyfunc flavor. However, the `mlflow.statsmodels` flavor completely omits this guard. An attacker who places a crafted MLmodel artifact into any accessible artifact store can trigger arbitrary code execution on any process that calls `mlflow.pyfunc.load_model()` against the malicious model — **even w

• Обновитесь до 3.15.0

Hugging Face Transformers library writes remote code to disk prior to consent check

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

pypdf: Inefficient handling of non-whitespace inputs in read_until_whitespace

pypdf is a free and open-source pure-python PDF library. Prior to 6.15.0, an attacker can craft a PDF that causes long runtimes when the pypdf/_utils.py function read_until_whitespace reads a stream containing a long run of bytes without whitespace. The function repeatedly performs immutable bytes concatenation in a one-byte loop, causing quadratic processing cost for the long non-whitespace input. This issue is fixed in version 6.15.0.

• Обновитесь до 6.15.0
• Обновитесь до 6.19.0-1