A Systematic Study of Code Obfuscation against LLM-Based Vulnerability Detection

As large language models LLMs are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse vulnerability types have become a pressing concern. In traditional adversarial settings, code obfuscation has long been used as a general strategy to bypass...

Jailbreak-Zero: A Path to Pareto Optimal Red Teaming for Large Language Models

This paper introduces Jailbreak-Zero, a novel red teaming methodology that shifts the paradigm of Large Language Model LLM safety evaluation from a constrained example-based approach to a more expansive and effective policy-based framework. By leveraging an attack LLM to generate a high volume of...

Large Language Models As a (Bad) Security Norm in the Context of Regulation and Compliance

The use of Large Language Models LLM by providers of cybersecurity and digital infrastructures of all kinds is an ongoing development. It is suggested and on an experimental basis used to write the code for the systems, and potentially fed with sensitive data or what would otherwise be considered...

CVE-2025-33212

Summary: NVIDIA NeMo Framework’s model-loading vulnerability could enable code execution, privilege escalation, DoS, or data tampering when loading a malicious file. Root cause: improper control during file/model loading. Impact: HIGH across confidentiality, integrity, and availability. Exploitat...

UIXPOSE: Mobile Malware Detection Via Intention-Behaviour Discrepancy Analysis

We introduce UIXPOSE, a source-code-agnostic framework that operates on both compiled and open-source apps. This framework applies Intention Behaviour Alignment IBA to mobile malware analysis, aligning UI-inferred intent with runtime semantics. Previous work either infers intent statically, e.g.,...

Trust in LLM-Controlled Robotics: A Survey of Security Threats, Defenses and Challenges

The integration of Large Language Models LLMs into robotics has revolutionized their ability to interpret complex human commands and execute sophisticated tasks. However, such paradigm shift introduces critical security vulnerabilities stemming from the ''embodiment gap'', a discord between the...

PentestEval: Benchmarking LLM-Based Penetration Testing with Modular and Stage-Level Design

Penetration testing is essential for assessing and strengthening system security against real-world threats, yet traditional workflows remain highly manual, expertise-intensive, and difficult to scale. Although recent advances in Large Language Models LLMs offer promising opportunities for...

SeBERTis: A Framework for Producing Classifiers of Security-Related Issue Reports

Monitoring issue tracker submissions is a crucial software maintenance activity. A key goal is the prioritization of high risk, security-related bugs. If such bugs can be recognized early, the risk of propagation to dependent products and endangerment of stakeholder benefits can be mitigated. To...

CVE-2025-13824 Micro820®, Micro850®, Micro870® – Specialized Fuzzing Vulnerabilities

A security issue exists due to improper handling of malformed CIP packets during fuzzing. The controller enters a hard fault with solid red Fault LED and becomes unresponsive. Upon power cycle, the controller will enter recoverable fault where the MS LED and Fault LED become flashing red and...

Security and Detectability Analysis of Unicode Text Watermarking Methods against Large Language Models

Securing digital text is becoming increasingly relevant due to the widespread use of large language models. Individuals' fear of losing control over data when it is being used to train such machine learning models or when distinguishing model-generated output from text written by humans. Digital...

One Leak Away: How Pretrained Model Exposure Amplifies Jailbreak Risks in Finetuned LLMs

Finetuning pretrained large language models LLMs has become the standard paradigm for developing downstream applications. However, its security implications remain unclear, particularly regarding whether finetuned LLMs inherit jailbreak vulnerabilities from their pretrained sources. We investigat...

The Role of AI in Modern Penetration Testing

Penetration testing is a cornerstone of cybersecurity, traditionally driven by manual, time-intensive processes. As systems grow in complexity, there is a pressing need for more scalable and efficient testing methodologies. This systematic literature review examines how Artificial Intelligence AI...

Diverse LLMs Vs. Vulnerabilities: Who Detects and Fixes Them Better?

Large Language Models LLMs are increasingly being studied for Software Vulnerability Detection SVD and Repair SVR. Individual LLMs have demonstrated code understanding abilities, but they frequently struggle when identifying complex vulnerabilities and generating fixes. This study presents...

Development Team Augmentation: A Strategic Approach for High-Performance Teams

Scale software teams fast with development team augmentation. Learn when it works best, key models, common mistakes, and how to choose the right partner...

Persistent Backdoor Attacks under Continual Fine-Tuning of LLMs

Backdoor attacks embed malicious behaviors into Large Language Models LLMs, enabling adversaries to trigger harmful outputs or bypass safety controls. However, the persistence of the implanted backdoors under user-driven post-deployment continual fine-tuning has been rarely examined. Most prior...

Scale AI Securely with Qualys TotalAI’s Streamlined Onboarding, Deeper Risk Detection, and Compliance-Ready Reporting

Executive Summary Enterprises are entering a phase where AI systems function as decision engines that shape customer interactions, operational workflows, and business outcomes. This creates a new class of risk that is behavioral, contextual, and dynamic, driven by how models interpret instruction...

Patch Wednesday: Root Cause Analysis with LLMs

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Webinar: How Attackers Exploit Cloud Misconfigurations Across AWS, AI Models, and Kubernetes

Cloud security is changing. Attackers are no longer just breaking down the door; they are finding unlocked windows in your configurations, your identities, and your code. Standard security tools often miss these threats because they look like normal activity. To stop them, you need to see exactly...

Defining Cost Function of Steganography with Large Language Models

In this paper, we make the first attempt towards defining cost function of steganography with large language models LLMs, which is totally different from previous works that rely heavily on expert knowledge or require large-scale datasets for cost learning. To achieve this goal, a two-stage...

LLM-PEA: Leveraging Large Language Models against Phishing Email Attacks

Email phishing is one of the most prevalent and globally consequential vectors of cyber intrusion. As systems increasingly deploy Large Language Models LLMs applications, these systems face evolving phishing email threats that exploit their fundamental architectures. Current LLMs require...