CVE-2026-5654

AMR-NB codec crash in Wireshark 4.6.0 to 4.6.4 and 4.4.0 to 4.4.14 allows denial of service...

PT-2026-36054

Name of the Vulnerable Software and Affected Versions Wireshark versions 4.6.0 through 4.6.4 Wireshark versions 4.4.0 through 4.4.14 Description A crash in the AMR-NB codec allows for a denial of service. Recommendations At the moment, there is no information about a newer version that contains a...

UBUNTU-CVE-2026-37555

An issue was discovered in libsndfile 1.2.2 IMA ADPCM codec. The AIFF code path line 241 was fixed with sfcountt cast, but the WAV code path line 235 and close path line 167 were not. When samplesperblock int blocks int exceeds INTMAX, the 32-bit multiplication overflows before being assigned to...

CVE-2026-37555

An issue was discovered in libsndfile 1.2.2 IMA ADPCM codec. The AIFF code path line 241 was fixed with sfcountt cast, but the WAV code path line 235 and close path line 167 were not. When samplesperblock int blocks int exceeds INTMAX, the 32-bit multiplication overflows before being assigned to...

CVE-2026-4313 Stored XSS in AdaptiveGRC

AdaptiveGRC is vulnerable to Stored XSS via text type fields across the forms. Authenticated attacker can replace the value of the text field in the HTTP POST request. Improper parameter validation by the server results in arbitrary JavaScript execution in the victim's browser. Critically, this...

Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective

Machine learning-based malware detectors are increasingly vulnerable to adversarial examples. Traditional defenses, such as one-shot adversarial training, often fail against adaptive attackers who use reinforcement learning to bypass detection. This paper proposes a robust defense framework based...

Adaptive Instruction Composition for Automated LLM Red-Teaming

Many approaches to LLM red-teaming leverage an attacker LLM to discover jailbreaks against a target. Several of them task the attacker with identifying effective strategies through trial and error, resulting in a semantically limited range of successes. Another approach discovers diverse attacks ...

ARES: Adaptive Red-Teaming and End-To-End Repair of Policy-Reward System

Reinforcement Learning from Human Feedback RLHF is central to aligning Large Language Models LLMs, yet it introduces a critical vulnerability: an imperfect Reward Model RM can become a single point of failure when it fails to penalize unsafe behaviors. While existing red-teaming approaches...

exploitra

🚀 EXPLOITRA v1.0 - Advanced Vulnerability Engine EXPLOITRA...

MLDAS: Machine Learning Dynamic Algorithm Selection for Software-Defined Networking Security

Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning ML algorithms with Software-Defined Networking SDN controllers to enhan...

ADAM: A Systematic Data Extraction Attack on Agent Memory Via Adaptive Querying

Large Language Model LLM agents have achieved rapid adoption and demonstrated remarkable capabilities across a wide range of applications. To improve reasoning and task execution, modern LLM agents would incorporate memory modules or retrieval-augmented generation RAG mechanisms, enabling them to...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

The agentic SOC—Rethinking SecOps for the next decade

Every major shift in cyberattacker behavior over the past decade has followed a meaningful shift in how defenders operate. When security operation centers SOCs deployed endpoint detection and response EDR—and later extended detection and response XDR—security teams raised the bar, pushing...

Your Agent Is Mine: Measuring Malicious Intermediary Attacks on the LLM Supply Chain

Large language model LLM agents increasingly rely on third-party API routers to dispatch tool-calling requests across multiple upstream providers. These routers operate as application-layer proxies with full plaintext access to every in-flight JSON payload, yet no provider enforces cryptographic...

LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Although recent large language models LLMs have shown promise in translating low-level representations into high-level...

Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses under White-Box and Black-Box Threats

Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied separately, their combination, the adversarial robustness of drift-adaptive detectors, remains unexplored. We address this problem with AdvDA, a rece...

PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy

While recent approaches leverage large language models LLMs and multi-agent pipelines to automatically generate proof-of-concept PoC exploits from vulnerability reports, existing systems often suffer from two fundamental limitations: unreliable validation based on surface-level execution signals...

Triggering and Detecting Exploitable Library Vulnerability from the Client by Directed Greybox Fuzzing

Developers utilize third-party libraries to improve productivity, which also introduces potential security risks. Existing approaches generate tests for public functions to trigger library vulnerabilities from client programs, yet they depend on proof-of-concepts PoCs, which are often unavailable...

Red-MIRROR: Agentic LLM-Based Autonomous Penetration Testing with Reflective Verification and Knowledge-Augmented Interaction

Web applications remain the dominant attack surface in cybersecurity, where vulnerabilities such as SQL injection, XSS, and business logic flaws continue to cause significant data breaches. While penetration testing is effective for identifying these weaknesses, traditional manual approaches are...

CLSA-2026-1774438942 freerdp: Fix of 3 CVEs

CVE-2026-29774: heap buffer overflow in H.264 AVC420/AVC444 YUV-to-RGB conversion - CVE-2026-31884: division by zero in ADPCM decoders when nBlockAlign is 0 - CVE-2026-29775: off-by-one in bitmapcacheput...