A heap-based buffer overflow was found in dnsmasq. When DNSSEC validation and query logging are both enabled, logging of DS or DNSKEY replies containing unsupported algorithm or digest types can cause dnsmasq to write past the end of an internal logging buffer. A remote attacker able to supply such a DNS response may crash the dnsmasq process, resulting in denial of service.

A use-after-free vulnerability was found in FFmpeg's RASC video decoder. The decode_move() function initializes a read pointer into a decompressed buffer, but a subsequent reallocation of that same buffer during move-table processing leaves the pointer dangling. An attacker could exploit this by providing a specially crafted AVI file containing a malicious RASC video stream. When a user opens or plays the file, the decoder reads from freed heap memory, which could lead to a denial of service (crash).

A path traversal vulnerability was found in pulpcore. The relative_path_validator function only verifies that content paths do not begin with "/" but fails to block directory traversal sequences such as "../" anywhere in the path. An authenticated administrator can craft a relative_path containing embedded traversal sequences (e.g., "looking/normal/../../../../etc/shadow") that escapes the intended export directory during FilesystemExport operations. Because the file content is also user-controlled (uploaded artifact), this allows arbitrary file write to any location writable by the Pulp service user, potentially leading to service compromise or further system exploitation.

The issue is unexpected program termination based on ordering and/or specific content in responses to queries for CNAME or DNAME, and A records. Specifically, if a client queries for a DNAME and A record below the DNAME to the resolver, and the authoritative server responds positively to the A query but delays the DNAME response and later responds negatively, `named` may quit unexpectedly. Or, if a client queries for a CNAME and A record for the same name to the resolver, and the authoritative server responds positively to the A query but delays the CNAME response and later responds with a self-referential CNAME, the same failure may occur. This issue affects BIND 9 versions 9.18.0 through 9.18.50, 9.20.0 through 9.20.24, 9.18.11-S1 through 9.18.50-S1, and 9.20.9-S1 through 9.20.24-S1.

A flaw was found in sssd. When authenticating with a YubiKey, the SSSD PAM responder can crash due to a use-after-free vulnerability, where a memory pointer is incorrectly handled. A local attacker could exploit this flaw by manipulating smartcard or YubiKey contents, leading to a denial of service that disrupts authentication. This vulnerability also presents a potential for privilege escalation, although it is difficult to exploit.

Invalid memory access in Sentencepiece versions less than 0.2.1 when using a vulnerable model file, which is not created in the normal training procedure.

Impact: In body-parser versions prior to 1.20.6 (1.x line) and 2.3.0 (2.x line), when the parser is configured with an invalid limit option value such as an unparseable string or NaN, bytes.parse returns null and the request body size check is silently skipped. Applications that rely on limit as their primary safeguard against oversized request bodies will accept arbitrarily large payloads, leading to excessive memory and CPU usage and denial of service. Patches: This issue is fixed in body-parser 1.20.6 and 2.3.0. After the fix, invalid limit values throw a clear error at parser construction time instead of silently disabling enforcement, while null and undefined continue to fall back to the default limit of 100kb. Workarounds: Validate the limit value before passing it to body-parser. For example, parse the value at startup and reject any configuration where the result is null or a non-finite number.

A vulnerability in keras-team/keras versions <= 3.15.0 allows for a denial of service (DoS) attack when loading malicious .keras model files via the keras.models.load_model() function. The H5IOStore.__getitem__ method in keras/src/saving/saving_lib.py does not validate the shape or size of datasets, leading to unbounded memory allocation. A specially crafted .keras file can exploit this flaw to trigger an out-of-memory (OOM) condition, causing the process to be terminated (exit code 137). This issue bypasses the fix for CVE-2026-0897, which only addressed a similar vulnerability in KerasFileEditor. The attack vector includes poisoned models from public repositories or malicious model registries, posing a risk to machine learning pipelines that process untrusted models.

The fix for CVE-2026-2443 was regressed by a subsequent rework commit that replaced specific overflow checks with a general signed comparison. When a client sends a Range request with a suffix length exceeding the content size, the resulting negative start value is not properly clamped, leading to malformed HTTP 206 responses and log flooding.

A heap out-of-bounds read flaw was found in libsoup. When parsing multipart HTTP messages, an integer type mismatch between the caller and soup_headers_parse() can cause the length parameter to be incorrectly truncated, leading to a heap buffer over-read. A remote attacker could use this flaw to crash an application using libsoup or potentially disclose heap memory contents.

SoupAuthManager caches proxy authentication credentials without scoping them to the proxy authority (host:port). When the proxy configuration changes (e.g., via system settings or WPAD), cached Proxy-Authorization headers from the previous proxy are sent to the new proxy, leaking credentials.

A flaw was found in 389 Directory Server in the __aclp__normalize_acltxt() function of aclparse.c. A malformed ACI (Access Control Instruction) string can trigger heap-buffer-overflow writes and reads during ACI parsing. The function fails to validate that the ACI keyword has sufficient length after whitespace stripping, leading to a 1-byte out-of-bounds write and subsequent out-of-bounds reads. An authenticated user with write access to the aci attribute could send a crafted ACI value to silently corrupt heap memory in the directory server process.

A flaw was found in Katello's of Red Hat Satellite. A content upload functionality where insufficient authorization checks in the ContentUploadsController allowed users with the edit_products permission to query content information for repositories outside the products they were authorized to manage. An authenticated attacker could exploit this issue to determine whether specific content exists within repositories that should otherwise be inaccessible. This issue does not allow unauthorized modification, import, or publication of content.

A flaw was found in the cifs-utils package where the cifs.upcall helper fails to securely drop its root privileges before looking up user information inside a user-controlled environment. A local, low privileged attacker can exploit this by using a crafted request_key payload to trick the root-owned helper into entering a custom environment (namespace) containing a malicious NSS module. This forces the system to load the attacker's controlled NSS Module and configuration, allowing them to execute arbitrary commands as the root user, elevating their privileges and fully compromising the system.

A flaw was found in vLLM, an open-source library for large language model inference. This vulnerability arises from improper handling of image metadata, specifically EXIF orientation and PNG transparency (tRNS) data, during image processing. When images are converted to RGB, transparency information may be implicitly discarded or remapped, leading to unexpected rendering of transparent pixels and distortion of input content. This can result in the model misinterpreting image content, potentially affecting the integrity of processed data.

When a provide-xfr is given with a tls-auth-name, a secondary requesting a transfer should provide a client certificate with that name. However, no client certificate is needed when the request comes in over TLS over the regular tls-port (and not the tls-auth-port) or over over TCP over the regular port, when the other conditions of the provide-xfr rule match.

A vulnerability in keras-team/keras version 3.15.0 allows unsafe deserialization of attacker-controlled PyTorch pickle data through the public `keras.layers.TorchModuleWrapper.from_config` method. This method invokes `torch.load(..., weights_only=False)` without requiring an explicit unsafe opt-in, such as a `safe_mode=False` parameter. When called outside a `SafeModeScope(True)` context, the absence of an ambient safe mode state permits unsafe deserialization by default. This issue can lead to arbitrary code execution if untrusted Keras layer configurations are processed using this method. The vulnerability arises because the method does not enforce safe deserialization practices unless explicitly guarded by Keras safe mode.

A vulnerability in keras-team/keras version 3.12.0 allows an attacker to craft a malicious tar archive that bypasses the `filter_safe_tarinfos` validation in `keras/src/utils/file_utils.py`. Specifically, symlink entries are not subjected to the same `is_path_in_dir` validation as regular file entries, allowing symlinks to be created outside the intended extraction directory. This can lead to symlink-based file read, file overwrite, or directory escape attacks. The issue is particularly impactful on Python 3.10 and 3.11, where `filter_safe_tarinfos` is the sole defense against tar path traversal. This vulnerability is distinct from CVE-2025-12060 and other previously reported issues.

A vulnerability in keras-team/keras version 3.14.0 allows for arbitrary code execution due to improper handling of deserialization in the `Lambda` layer. Specifically, the `_raise_for_lambda_deserialization()` function fails to enforce the safe-mode guard when `safe_mode` is set to `None`, which is the default value when `from_config()` is called outside of a `SafeModeScope` context. This logic error conflates `None` (unset/default-deny) with `False` (explicitly disabled), bypassing the guard and allowing attacker-controlled `marshal` bytecode to be deserialized. Affected call sites include `keras.layers.deserialize(config)`, `keras.models.clone_model(model)`, and any direct invocation of `Lambda.from_config(config)` without an enclosing `SafeModeScope(True)`. This vulnerability can be exploited to achieve arbitrary OS-level code execution in the context of the server or user process.

Keras versions up to and including 3.13.2 are vulnerable to an arbitrary HDF5 file read due to an incomplete fix for CVE-2026-1669. The vulnerability resides in the `H5IOStore._verify_dataset()` and `file_editor.py` methods, which fail to check the `dataset.is_virtual` property of HDF5 datasets. This allows an attacker to craft a malicious `.keras` model archive or `.h5` weights file containing a Virtual Dataset (VDS) that references external HDF5 files on the victim's filesystem. When the victim loads the model using `keras.models.load_model()` or `keras.saving.load_model()`, the external file is transparently read, leading to potential information disclosure. Fixed in versions 3.12.2 and 3.14.1.