Schwachstellen verstehen. Anwendungen schützen.
Erfahren Sie, welche Schwachstellen Ihre Technologien betreffen und wie Sie diese beheben. Hier finden Sie verständliche Erklärungen, betroffene Versionen und konkrete Empfehlungen - von CMS und Plugins bis zu Anwendungsbibliotheken.
Schwachstellenarchiv 452
Filter zurücksetzenMCP Atlassian: JIRA_PROJECTS_FILTER / CONFLUENCE_SPACES_FILTER allow forbidden-project content exfiltration (one LIVE-proven on Atlassian Cloud)
psd-tools composite/numpy has uncontrolled memory allocation via crafted PSD geometry
MCP Atlassian: MCP HTTP Client Server-Local File Exfiltration via Unvalidated Attachment Upload Path
Soup Sieve: Polynomial-time ReDoS (O(n²)) in the `IDENTIFIER` / `VALUE` selector sub-patterns
Soup Sieve: Polynomial-time ReDoS (O(n²)) in the whitespace/comment trimming regex `RE_WS_END` (triggers on VALID selectors)
LMDeploy vulnerable to arbitrary code execution via eval() of untrusted quant_dtype in model config loading
AnyIO process-pool workers can block indefinitely on undrained stderr
sanic chunked trailer request smuggling allows hidden second request execution
LMdeploy has Remote Code Execution by Pickle Deserialization via zmq_rpc.call_and_response() in InterLM/lmdeploy
PickleScan has multiple stdlib modules with direct RCE not in blocklist
JupyterLab: Cross-site scripting (XSS) in JupyterLab via crafted language package (jupyterlab.json)
JupyterLab: Argument injection in JupyterLab extension uninstall exposes server-readable files and internal URLs
Downloaded seed wheels (pip/setuptools) are not integrity-checked before use
tornado: CurlAsyncHTTPClient enforces no response-size limit — decompression bomb drives unbounded memory accumulation to OOM
Tornado: StaticFileHandler follows symlinks outside static root (path traversal)
MONAI: Unsafe deserialization in NumpyReader allows arbitrary code execution via malicious .npy files
Incomplete Fix in MONAI: algo_from_pickle() pickle.loads() RCE still present in v1.5.2 despite GHSA-89gg-p5r5-q6r4 claiming patch
In MONAI 1.6.0, PersistentDataset (monai/data/dataset.py) explicitly rejects the combination track_meta=True with weights_only=True, forcing users who cache MetaTensors (the default tensor type in MON
Decepticon: Role-boundary forgery via ChatML special-token literals in web crawl output composed into LLM context
Windows ML CLI: CORS misconfig enables localhost RCE
vLLM: SSRF + arbitrary local file read in MiMoV2OmniMultiModalProcessor `_fetch_image` and audio loader bypass MediaConnector protections
vLLM: Unauthenticated Internal Path and Username Disclosure via Validation Error Messages
unstructured: Server-Side Request Forgery in the URL-based partitioning
Erkennen Sie Schwachstellen in Ihrer Anwendung
Mit der DockRay-Integration und aktivierter Softwareinventarisierung prüft DockRay gemeldete Komponentenversionen auf bekannte Schwachstellen und benachrichtigt Sie über bestätigte Treffer. Die Abdeckung hängt von der unterstützten Technologie und den verfügbaren Meldungen ab.