5061 matches found
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...
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...
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...
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...
The vulnerability of the scheduleAdd() function (/goform/scheduleAdd) in the microprogramming software for Linksys RE6250, RE6300, RE6350, RE6500, RE7000, and RE9000 allows a hacker to execute arbitrary code.
The vulnerability of the scheduleAdd function /goform/scheduleAdd in the microprogramming software for Linksys RE6250, RE6300, RE6350, RE6500, RE7000, and RE9000 is related to buffer overflow in the stack. Exploiting this vulnerability could allow an attacker to execute arbitrary code remotely...
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...
Chasing Shadows: Pitfalls in LLM Security Research
Large language models LLMs are increasingly prevalent in security research. Their unique characteristics, however, introduce challenges that undermine established paradigms of reproducibility, rigor, and evaluation. Prior work has identified common pitfalls in traditional machine learning researc...
The vulnerability of the addStaticRoute() function (/goform/addStaticRoute) in the microprogramming software for Linksys RE6250, RE6300, RE6350, RE6500, RE7000, and RE9000 allows a hacker to execute arbitrary commands.
The vulnerability of the addStaticRoute function /goform/addStaticRoute in Linksys RE6250, RE6300, RE6350, RE6500, RE7000, and RE9000 routers lies in the lack of measures to neutralize specific elements. Exploiting this vulnerability allows a remote attacker to execute arbitrary commands...
CVE-2025-12946
CVE-2025-12946 affects NETGEAR Nighthawk routers and related models (RS700, RAX54Sv2, RAX41v2, RAX50, RAXE500, RAX41, RAX43, RAX35v2, RAXE450, RAX43v2, RAX42, RAX45, RAX50v2, MR90, MS90, RAX42v2, RAX49S). Root cause is improper input validation in the speedtest feature, enabling WAN-side attacker...
CVE-2025-12946 Improper input validation in NETGEAR Nighthawk routers
A vulnerability in the speedtest feature of affected NETGEAR Nighthawk routers, caused by improper input validation, can allow attackers on the router's WAN side, using attacker-in-the-middle techniques MiTM to manipulate DNS responses and execute commands when speedtests are run. This issue...
Prompt injection is a problem that may never be fixed, warns NCSC
Prompt injection is shaping up to be one of the most stubborn problems in AI security, and the UK’s National Cyber Security Centre NCSC has warned that it may never be “fixed” in the way SQL injection was. Two years ago, the NCSC said prompt injection might turn out to be the “SQL injection of th...
CVE-2025-40935
Summary: CVE-2025-40935 affects Siemens/RUGGEDCOM ROS/RMC8388 and related devices. The issue is an input validation error during the TLS certificate upload in the web service, which could allow an authenticated remote attacker to crash and reboot the device, causing a temporary Denial of Service....
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities across Clinical Specialties
Medical Large Language Models LLMs are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU cluster...
Yottamaster多款产品 安全漏洞
Yottamaster DM2 and others are a private cloud hard disk enclosure from Yottamaster. A security vulnerability exists in several Yottamaster products, which stems from a path traversal vulnerability in the file upload component. The following products and versions are affected: Yottamaster DM2, DM...
