Whispering Agents: an Event-Driven Covert Communication Protocol for the Internet of Agents

The emergence of the Internet of Agents IoA introduces critical challenges for communication privacy in sensitive, high-stakes domains. While standard Agent-to-Agent A2A protocols secure message content, they are not designed to protect the act of communication itself, leaving agents vulnerable t...

PT-2025-31774 · Undefined · Undefined

hey @Microsoft when Defender started scanning for non-malicious documents like pandemic compliance forms, it crossed from security into behavioral monitoring. Microsoft's transparency failures around this documented in CVE-2020-16883 validated many professionals' concerns...

PHASE: Passive Human Activity Simulation Evaluation

Cybersecurity simulation environments, such as cyber ranges, honeypots, and sandboxes, require realistic human behavior to be effective, yet no quantitative method exists to assess the behavioral fidelity of synthetic user personas. This paper presents PHASE Passive Human Activity Simulation...

A Crowdsensing Intrusion Detection Dataset for Decentralized Federated Learning Models

This paper introduces a dataset and experimental study for decentralized federated learning DFL applied to IoT crowdsensing malware detection. The dataset comprises behavioral records from benign and eight malware families. A total of 21,582,484 original records were collected from system calls,...

LLM-Stackelberg Games: Conjectural Reasoning Equilibria and Their Applications to Spearphishing

We introduce the framework of LLM-Stackelberg games, a class of sequential decision-making models that integrate large language models LLMs into strategic interactions between a leader and a follower. Departing from classical Stackelberg assumptions of complete information and rational agents, ou...

Towards Reliable Forgetting: a Survey on Machine Unlearning Verification, Challenges, and Future Directions

With growing demands for privacy protection, security, and legal compliance e.g., GDPR, machine unlearning has emerged as a critical technique for ensuring the controllability and regulatory alignment of machine learning models. However, a fundamental challenge in this field lies in effectively...

The data on denying social media for kids (re-air) (Lock and Code S06E12)

This week on the Lock and Code podcast … Complex problems often assume complex solutions, but recent observations about increased levels of anxiety and depression, increased reports of loneliness, and lower rates of in-person friendships for teens and children in America today have led some schoo...

MalGEN: a Generative Agent Framework for Modeling Malicious Software in Cybersecurity

The dual use nature of Large Language Models LLMs presents a growing challenge in cybersecurity. While LLM enhances automation and reasoning for defenders, they also introduce new risks, particularly their potential to be misused for generating evasive, AI crafted malware. Despite this emerging...

Zero-Trust Foundation Models: a New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things

This paper focuses on Zero-Trust Foundation Models ZTFMs, a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models FMs for Internet of Things IoT systems. By integrating core tenets, such as continuous verification, least privilege access LPA, data...

RADEP: a Resilient Adaptive Defense Framework against Model Extraction Attacks

Machine Learning as a Service MLaaS enables users to leverage powerful machine learning models through cloud-based APIs, offering scalability and ease of deployment. However, these services are vulnerable to model extraction attacks, where adversaries repeatedly query the application programming...

Key Takeaways from the Take Command Summit 2025: Inside the Mind of an Attacker

In one of the most anticipated sessions of Take Command 2025, Raj Samani, Chief Scientist at Rapid7, sat down with Trent Teyema, former FBI Special Agent and President of CSG Strategies, for a candid conversation on how threat actors are evolving and what defenders must do to keep up. Moderated b...

More AIs Are Taking Polls and Surveys

I already knew about the declining response rate for polls and surveys. The percentage of AI bots that respond to surveys is also increasing. Solutions are hard: 1. Make surveys less boring. We need to move past bland, grid-filled surveys and start designing experiences people actually want to...

Real-Time Detection of Insider Threats Using Behavioral Analytics and Deep Evidential Clustering

Insider threats represent one of the most critical challenges in modern cybersecurity. These threats arise from individuals within an organization who misuse their legitimate access to harm the organization's assets, data, or operations. Traditional security mechanisms, primarily designed for...

Modeling Behavioral Preferences of Cyber Adversaries Using Inverse Reinforcement Learning

This paper presents a holistic approach to attacker preference modeling from system-level audit logs using inverse reinforcement learning IRL. Adversary modeling is an important capability in cybersecurity that lets defenders characterize behaviors of potential attackers, which enables attributio...

AI-Driven IRM: Transforming Insider Risk Management with Adaptive Scoring and LLM-Based Threat Detection

Insider threats pose a significant challenge to organizational security, often evading traditional rule-based detection systems due to their subtlety and contextual nature. This paper presents an AI-powered Insider Risk Management IRM system that integrates behavioral analytics, dynamic risk...

Can LLMs Handle WebShell Detection? Overcoming Detection Challenges with Behavioral Function-Aware Framework

WebShell attacks, in which malicious scripts are injected into web servers, are a major cybersecurity threat. Traditional machine learning and deep learning methods are hampered by issues such as the need for extensive training data, catastrophic forgetting, and poor generalization. Recently, Lar...

CVE-2025-2323

A vulnerability was found in 274056675 springboot-openai-chatgpt e84f6f5. It has been declared as problematic. This vulnerability affects the function updateQuestionCou of the file /api/mjkj-chat/chat/mng/update/questionCou of the component Number of Question Handler. The manipulation leads to...

CVE-2025-2323

A vulnerability was found in 274056675 springboot-openai-chatgpt e84f6f5. It has been declared as problematic. This vulnerability affects the function updateQuestionCou of the file /api/mjkj-chat/chat/mng/update/questionCou of the component Number of Question Handler. The manipulation leads to...

CVE-2025-2323 274056675 springboot-openai-chatgpt Number of Question questionCou updateQuestionCou behavioral workflow

A vulnerability was found in 274056675 springboot-openai-chatgpt e84f6f5. It has been declared as problematic. This vulnerability affects the function updateQuestionCou of the file /api/mjkj-chat/chat/mng/update/questionCou of the component Number of Question Handler. The manipulation leads to...

CVE-2025-2323 274056675 springboot-openai-chatgpt Number of Question questionCou updateQuestionCou behavioral workflow

A vulnerability was found in 274056675 springboot-openai-chatgpt e84f6f5. It has been declared as problematic. This vulnerability affects the function updateQuestionCou of the file /api/mjkj-chat/chat/mng/update/questionCou of the component Number of Question Handler. The manipulation leads to...