Base de CVEs

GitPython is a python library used to interact with Git repositories. Prior to 3.2.1, create_submodule() in git/objects/submodule/base.py accepts an attacker-influenced submodule path such as .git/hooks and invokes _clone_repo() before safely rejecting the destination. In 3.1.x, _validate_repo_path() does not exist and the call succeeds silently, while in 3.2.0 and later affected revisions the check in git/index/base.py raises ValueError only after the clone has written to disk and does not remove the planted file. An attacker-controlled submodule can therefore place an executable post-checkout hook that a later Git operation executes. Exploitation requires an application to accept an untrusted submodule path and a clone destination such as .git/hooks that is empty enough for Git to clone into it, which commonly means that default sample hooks are absent. This issue is fixed in version 3.2.1.

toml-node is a TOML parser for Node.js and the browser. Prior to 5.0.1, toml.parse() passes unbounded hexadecimal, octal, binary, and decimal digit sequences from lib/parser.js to BigInt() before reduceInteger() in lib/compiler.js checks whether the resulting value fits the TOML 64-bit integer range. Converting a sufficiently long integer string consumes super-linear CPU time and synchronously blocks the Node.js event loop, allowing one attacker-supplied TOML document to deny service to concurrent work in the process. The later range check cannot prevent the conversion cost because it executes after BigInt(). This issue is fixed in version 5.0.1.

toml-node is a TOML parser for Node.js and the browser. Prior to 5.0.1, toml.parse() repeatedly slices and rejoins every path prefix in lib/compiler.js while processing long dotted keys, table headers, array-of-tables headers, and inline-table keys in assign(), deepRef(), and setNestedKey(). The resulting quadratic work can block the Node.js event loop for several seconds, and sufficiently long paths can terminate the process through memory exhaustion. The maxDepth option does not limit key-segment count and therefore does not mitigate this path. This issue is fixed in version 5.0.1.

toml-node is a TOML parser for Node.js and the browser. Prior to 5.0.1, toml.parse() can write attacker-controlled properties to Object.prototype because quoteDottedString() in lib/compiler.js produces the same bookkeeping string for distinct quoted key paths, allowing addTableArray() to clear another key's guard. The assign() path then descends through an unowned Date value without applying the owned-container check and can reach the prototype chain. Objects created elsewhere in the process can inherit the injected property. This issue is fixed in version 5.0.1.

toml-node is a TOML parser for Node.js and the browser. Prior to 5.0.1, the toml.parse() compiler's addTableArray() function in lib/compiler.js rescans the complete assignedPaths and valueAssignments bookkeeping sets for every array-of-tables header. Distinct [[name]] paths are not pruned, so a document with many such headers causes quadratic synchronous work and can block the Node.js event loop, denying service to other operations in the process. This issue is fixed in version 5.0.1.

The nbconvert tool, jupyter nbconvert, converts Jupyter notebooks to various other formats via Jinja templates. From 5.4.0 until 7.17.2, ExtractOutputPreprocessor in nbconvert/preprocessors/extractoutput.py joins the attacker-controlled cell output metadata filename to output_files_dir without limiting the result to the intended directory. When a victim converts a malicious notebook with the markdown, LaTeX, reStructuredText, or AsciiDoc exporter, path traversal in the output filename can write attacker-controlled output data to arbitrary locations accessible to the victim. Running the notebook cell again replaces the malicious output metadata, but merely converting the supplied notebook does not. This issue is fixed in version 7.17.2.

Django REST framework is a toolkit for building Web APIs. Prior to 3.18.2, DefaultContentNegotiation.get_accept_list in rest_framework/negotiation.py accepts an unbounded number and aggregate size of Accept tokens, while select_renderer in that file repeatedly invokes _MediaType parsing from rest_framework/utils/mediatypes.py for each attacker-controlled token, every renderer, and forced 406 renegotiation. A remote unauthenticated client can send many distinct media types containing large quoted parameter values with numerous semicolons, causing Django's parameter parser and DRF's repeated parsing structure to consume disproportionate CPU. Requests to any reachable endpoint can pin synchronous workers for prolonged periods, and concurrent requests can make the service unavailable until the requests stop. This issue is fixed in version 3.18.2.

Django REST framework is a toolkit for building Web APIs. From 3.12.0 until 3.18.2, TokenProxy.delete() in rest_framework/authtoken/models.py uses the proxy's user-id pk while the concrete Token token-string primary key identifies rows, so Django issues a deletion query that matches no row and reports zero deletions without raising an error. Applications, administrative integrations, or operational scripts that revoke credentials through a TokenProxy instance can therefore believe revocation succeeded while the token remains live and continues authenticating. DRF's own TokenAdmin.delete_model workaround is not affected, but other callers can leave access available indefinitely because DRF tokens do not expire automatically. This issue is fixed in version 3.18.2.

Django REST framework is a toolkit for building Web APIs. From 3.6.4 until 3.18.2, JSONParser.parse in rest_framework/parsers.py uses the client-supplied Content-Type charset value with codecs.getreader, which accepts byte-transform codecs such as bz2_codec rather than only text encodings. A remote unauthenticated client can therefore cause a compressed JSON request body to be expanded after Django has enforced DATA_UPLOAD_MAX_MEMORY_SIZE against only the wire bytes, allowing a very small request to consume substantial per-worker memory. The default parser set is sufficient, and any reachable endpoint that accepts a JSON body is affected. This issue is fixed in version 3.18.2.

Jupyter Server is the backend for Jupyter web applications. From 2.0.0 until 2.6.0, enabling GatewayClient.url replaced the normal KernelWebsocketHandler with WebSocketChannelsHandler, which authenticated the user but did not call authorizer.is_authorized(...) for the execute action on the kernels resource. A restricted authenticated user could therefore open an upstream kernel channel and obtain kernel execution despite the configured Authorizer denying that action, exposing data and credentials available to the kernel and permitting modification or disruption of the kernel environment. The default gateway configuration is fixed in version 2.6.0. In 2.6.0 and later, the same authorization bypass remains possible when a deployment manually registers the deprecated jupyter_server.gateway.handlers module, because its handler still omits the configured kernel-execute authorization decision.

Missing verification of source of a communication channel in the default_bucket() function in Amazon SageMaker Python SDK V3 before 3.10.0 and V2 before 2.257.3 might allow remote actors to achieve code execution in the target SageMaker execution environment and obtain IAM credentials via pre-creation of a predictably named S3 bucket that the SDK uses without ownership validation. To remediate this issue, users should upgrade to Amazon SageMaker Python SDK 3.10.0 (V3) or 2.257.3 (V2).