Astra Linux – Vulnerability in Linux 6.12

In the Linux kernel, the following vulnerability has been resolved: Block: Fix to add folio to bio. A size of 4GB for folio is possible on some ARCHs, such as aarch64. A size of 16GB for hugepage is also supported. However, the “offset” of folio cannot be stored in “unsigned int”, which causes a...

Astra Linux – Vulnerability in containerd-app

Containerd is an open-source container runtime. A bug was discovered in Containerd prior to versions 1.6.38, 1.7.27, and 2.0.4. In these versions, containers launched with a User set as UID:GID that exceeded the maximum 32-bit signed integer could cause an overflow condition, resulting in the...

Astra Linux – Vulnerability found in Linux 6.1, Linux 6.12

In the Linux kernel, the following vulnerabilities have been resolved: RDMA/hns: Fixed a soft lockup that occurred during the loop that allocates BT pages. The driver executes a for-loop when allocating BT pages and mapping them with buffer pages. When allocating a large buffer e.g., an MR of ove...

Astra Linux – Vulnerability in libsoup3, libsoup2.4

A flaw was discovered in libsoup. The SoupWebsocketConnection may accept a large WebSocket message, which could cause libsoup to allocate memory and lead to a denial of service DoS attack...

Astra Linux – Vulnerability in unbound

Unbound versions up to and including 1.21.0 contain a vulnerability when handling replies with very large RRsets that it needs to perform name compression on. Malicious upstream responses with very large RRsets can cause Unbound to spend considerable time applying name compression to downstream...

ALSA-2025:9106 Moderate: git-lfs security update

Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server. Security Fixes: net/http: Request smuggling due to acceptance of invalid chunked data in net/http CVE-2025-22871...

Specification and Evaluation of Multi-Agent LLM Systems -- Prototype and Cybersecurity Applications

Recent advancements in LLMs indicate potential for novel applications, e.g., through reasoning capabilities in the latest OpenAI and DeepSeek models. For applying these models in specific domains beyond text generation, LLM-based multi-agent approaches can be utilized that solve complex tasks by...

ALSA-2025:9063 Moderate: git-lfs security update

Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server. Security Fixes: net/http: Request smuggling due to acceptance of invalid chunked data in net/http CVE-2025-22871...

ALSA-2025:9060 Moderate: git-lfs security update

Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server. Security Fixes: net/http: Request smuggling due to acceptance of invalid chunked data in net/http CVE-2025-22871...

Moderate: git-lfs security update

Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server. Security Fixes: net/http: Request smuggling due to acceptance of invalid chunked data in net/http CVE-2025-22871...

RHEL 9 : git-lfs (RHSA-2025:9078)

The remote Redhat Enterprise Linux 9 host has a package installed that is affected by a vulnerability as referenced in the RHSA-2025:9078 advisory. Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing th...

Moderate: git-lfs security update

Git Large File Storage LFS replaces large files such as audio samples, videos, datasets, and graphics with text pointers inside Git, while storing the file contents on a remote server. Security Fixes: net/http: Request smuggling due to acceptance of invalid chunked data in net/http CVE-2025-22871...

Step-By-Step Reasoning Attack: Revealing 'Erased' Knowledge in Large Language Models

Whitepaper called Step-By-Step Reasoning Attack: Revealing 'Erased' Knowledge In Large Language Models...

DEBIAN-CVE-2025-6052

A flaw was found in how GLib’s GString manages memory when adding data to strings. If a string is already very large, combining it with more input can cause a hidden overflow in the size calculation. This makes the system think it has enough memory when it doesn’t. As a result, data may be writte...

Towards Understanding the Cognitive Habits of Large Reasoning Models

Large Reasoning Models LRMs, which autonomously produce a reasoning Chain of Thought CoT before producing final responses, offer a promising approach to interpreting and monitoring model behaviors. Inspired by the observation that certain CoT patterns -- e.g., "Wait, did I miss anything?'' --...

SoK: Evaluating Jailbreak Guardrails for Large Language Models

Large Language Models LLMs have achieved remarkable progress, but their deployment has exposed critical vulnerabilities, particularly to jailbreak attacks that circumvent safety mechanisms. Guardrails--external defense mechanisms that monitor and control LLM interaction--have emerged as a promisi...

ELFuzz: Efficient Input Generation Via LLM-Driven Synthesis over Fuzzer Space

Generation-based fuzzing produces appropriate testing cases according to specifications of input grammars and semantic constraints to test systems and software. However, these specifications require significant manual efforts to construct. This paper proposes a new approach, ELFuzz Evolution...

Design Patterns for Securing LLM Agents against Prompt Injections

As AI agents powered by Large Language Models LLMs become increasingly versatile and capable of addressing a broad spectrum of tasks, ensuring their security has become a critical challenge. Among the most pressing threats are prompt injection attacks, which exploit the agent's resilience on...

Expert-In-The-Loop Systems with Cross-Domain and In-Domain Few-Shot Learning for Software Vulnerability Detection

As cyber threats become more sophisticated, rapid and accurate vulnerability detection is essential for maintaining secure systems. This study explores the use of Large Language Models LLMs in software vulnerability assessment by simulating the identification of Python code with known Common...

Mono: Is Your "Clean" Vulnerability Dataset Really Solvable? Exposing and Trapping Undecidable Patches and Beyond

The quantity and quality of vulnerability datasets are essential for developing deep learning solutions to vulnerability-related tasks. Due to the limited availability of vulnerabilities, a common approach to building such datasets is analyzing security patches in source code. However, existing...