Recent research in security AI is focusing on enhancing the detection and mitigation of vulnerabilities through advanced machine learning techniques. One significant area of development is the use of large language models (LLMs) for predicting security bug reports, where studies show that prompt-based models can identify potential issues with high sensitivity, though at the cost of increased false positives. Concurrently, new methodologies like WebSentinel are being introduced to detect prompt injection attacks, demonstrating improved effectiveness over existing solutions. Additionally, frameworks are being proposed to manage the hallucination risks associated with LLMs in security planning, which could streamline incident response processes by reducing recovery times significantly. Furthermore, the exploration of vulnerabilities in the deeper layers of LLMs through novel attack frameworks highlights the ongoing arms race between security measures and potential exploits. Collectively, these advancements suggest a shift towards more robust, reliable AI systems capable of addressing complex security challenges in real-world applications.
Investigating cybersecurity incidents requires collecting and analyzing evidence from multiple log sources, including intrusion detection alerts, network traffic records, and authentication events. Th...
LLM agents are increasingly relevant to research domains such as vulnerability discovery. Yet, the strongest systems remain closed and cloud-only, making them resource-intensive, difficult to reproduc...
Early detection of security bug reports (SBRs) is critical for timely vulnerability mitigation. We present an evaluation of prompt-based engineering and fine-tuning approaches for predicting SBRs usin...
Prompt injection attacks manipulate webpage content to cause web agents to execute attacker-specified tasks instead of the user's intended ones. Existing methods for detecting and localizing such atta...
Security incident analysis (SIA) poses a major challenge for security operations centers, which must manage overwhelming alert volumes, large and diverse data sources, complex toolchains, and limited ...
Artifact Evaluation (AE) is essential for ensuring the transparency and reliability of research, closing the gap between exploratory work and real-world deployment is particularly important in cyberse...
CAPTCHAs remain a critical defense against automated abuse, yet modern systems suffer from well-known limitations in usability, accessibility, and resistance to increasingly capable bots and low-cost ...
Phishing detectors built on engineered website features attain near-perfect accuracy under i.i.d.\ evaluation, yet deployment security depends on robustness to post-deployment feature manipulation. We...
Large language models (LLMs) are promising tools for supporting security management tasks, such as incident response planning. However, their unreliability and tendency to hallucinate remain significa...
Test Vector Leakage Assessment (TVLA) based on Welch's $t$-test has become a standard tool for detecting side-channel leakage. However, its mean-based nature can limit sensitivity when leakage manifes...