A Study of Cursorrules Files in GitHub Open Source Projects

Prompts are the primary mechanism for communicating with AI agents, and they directly influence the quality and reliability of AI-generated code. As AI-assisted programming becomes widely adopted, modern tools increasingly combine dynamic conversational prompts with static configuration-like prom...

Dueling Deep Q-Learning for Intrusion Detection

Intrusion detection systems IDS and automated systems for detecting and reporting cyber threats, are commonly handled via supervised machine learning methods. Though effective, these models struggle to effectively adapt to new attack types. This study proposes a novel approach by employing a...

Backdoor Decontamination Dynamics in LLM Agents

Open-weight LLM agents are vulnerable to backdoors installed during fine-tuning, which may be undetectable if the trigger conditions are never met during testing. Assuming defenders do not know the existing trigger, they cannot unlearn it directly. One decontamination strategy is to install a kno...

A Study of Kernel Telemetry Options for Security-Oriented Provenance

Provenance aims to capture the origins, transformations, and interactions of system objects for security and forensic applications. Existing provenance capture approaches still face major challenges and are not yet ready for production environments. In this paper, we first analyze the main kernel...

OpenSSH 10.5p1

OpenSSH is the premier connectivity tool for remote login with the SSH protocol. It encrypts all traffic to eliminate eavesdropping, connection hijacking, and other attacks. In addition, OpenSSH provides a large suite of secure tunneling capabilities, several authentication methods, and...

On Understanding, Identifying, and Mitigating Vulnerabilities in Agentic Large Language Models

Large Language Models LLMs have undergone a shift from stateless conversational interfaces to autonomous agents capable of multi-step planning, tool invocation, code execution, and maintaining persistent memory. When these agents operate with real-world privileges---calling APIs, modifying files,...

CISA: CDM Data Model Document 6.0.1

This document provides a comprehensive description of a common data schema that supports the associated Logical Data Model LDM to ensure that prescribed diagnostic activities within CDM solutions are consistent across all participating federal agencies. This updated version aligns with the latest...

When Agents Talk: Honeytokens under Shared Memory

During a 2026 cyber-capability evaluation, short-lived AI agents turned a shared package repository into persistent memory, passing exploit findings to later agents and rebuilding the channel after it was removed. The broader evaluation culminated in an intrusion into Hugging Face. This episode...

Once Poisoned, Arbitrarily Controlled: A Programmable Backdoor in VLMs

Existing vision-language model VLM backdoors are usually treated as static vulnerabilities: one-to-one and N-to-N attacks bind one or more triggers to a finite set of targets before victim training. This assumption substantially underestimates the threat. We show that a single poisoning phase can...

CATana: On the Dangers of SIM-Originating AT Commands

Hostile SIMs have been discussed as an attack vector against Mobile Equipment ME connected to cellular networks. One main attack path are proactive commands sent from the SIM to the victim device. In this work, the authors examine the threats posed by the RUN AT command which are SIM-originating...

Memoir: Learning, Verifying, and Evolving False-Positive Memories for Static Application Security Testing Tools

Static Application Security Testing SAST tools have become indispensable in modern secure software devel- opment. However, these tools often generate false-positive FP alerts, imposing substantial manual inspection costs and reducing the trust from developers. Existing FP reduction methods still...

Generative AI for Encrypted Traffic Analysis: Synthetic Dataset Generation and Classifier Evaluation

Network traffic analysis faces significant challenges with encrypted communications, primarily due to limited visibility into packet contents and the inherent imbalance in available datasets, particularly for anomalous traffic patterns. This paper addresses these challenges by exploring Generativ...

A Bird'S-Eye View on Security Considerations in RFCs

Request for comments RFCs are Internet standards, memorandums, and related technical documents about core Internet protocols made via and released by the Internet Engineering Task Force IETF. In the early 1990s each RFC was required to have a section for security considerations. The present work...

ColluSkill: Adversarial Cross-Skill Composition for Evading Agent Skill Scanners

Agent skills are emerging as an important attack surface in LLM-based agent systems. Through an empirical study of existing skill scanners, we find that current defenses mainly inspect individual skills, leaving risks from cross-skill composition insufficiently examined. This creates a practical...

Security Tests As Executable Specifications for LLM Code Generation: Benefits, Trade-Offs, and Coverage Limits

Large language models LLMs can generate functionally useful code that remains vulnerable, while security-focused interventions may break intended behavior. We investigate security tests as executable specifications both before generation and during iterative repair. We develop SecTDD, a controlle...

STAIR: Effective Incident Response Using an End-To-End Agentic Planning Framework

Incident response planning is critical for restoring compromised software systems after cyberattacks. Common practice relies on expert-driven playbooks that encode fixed response procedures, but these static workflows struggle to adapt to evolving incident states, changing recovery objectives, an...

Stealing Reasoning Traces from Proprietary LLM APIs

Leading large language model providers now conceal their models' step-by-step reasoning, or chain-of-thought, to protect intellectual property and limit information leakage. Rather than storing these traces server-side, providers return them to the client as blocks of encrypted text, which the...

Generating Attacks for LLMs with GFlowNets

The rapid advancement of Large Language Models LLMs has facilitated their ubiquitous integration into various domains, leading to widespread adoption. However, this escalating trend has introduced significant security vulnerabilities, necessitating the identification and mitigation of flaws arisi...

Measuring the Wrong Thing: Internal Harmfulness Scores Anti-Rank Successful Jailbreaks

Internal safety scores judge a prompt before any text is generated, and they are validated by how well they separate harmful prompts from benign ones. That separation is then read as evidence that the score will also catch the attacks that succeed. Harmful intent is a property of the prompt...

Joern 4.0.600

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...

You Are Not My Teammate: Behavioral Fingerprint-Based Detection of Suspicious Account Misuse

Online games have been continuously affected by cyber threats such as game bots and gold farming. Game bots, which are automated programs that play on behalf of human users, significantly accelerate character progression and reduce the engagement of legitimate players, potentially leading to user...

Nimux 1.0.5

Nimux is a native command surface for authorized security assessments. It combines network enumeration, credential validation, Active Directory operations, Kerberos workflows, remote execution, file movement, secrets collection, DCSync, GPO operations, database clients, and SOCKS routing into one...

Never Stop Speaking: A Denial-Of-Service Attack on End-To-End Speech Language Models

Many studies have shown that specially crafted inputs can induce large language models LLMs to generate excessively long outputs, resulting in significant computational overhead and resource consumption. While most existing denial-of-service DoS attacks target text-only LLMs, end-to-end E2E speec...

Pass-The-Passkey Family of Attacks

Research on passkey security uncovered three practically exploitable zero-day vulnerabilities in Windows 11 and Microsoft Entra ID. Two of them form a replay chain: Windows writes complete WebAuthn assertions to the event log for device-bound and hybrid authenticators, while Microsoft Entra ID...

Conversational Versus Dashboard Explainable AI for UAV Intrusion Detection: An Empirical Study of Operator Trust and Reliance

Machine learning-based Intrusion Detection Systems IDS have demonstrated superior performance in securing Unmanned Aerial Vehicle UAV networks. However, the 'black-box' nature of these models, combined with the high dimensionality of multimodal cyber-physical data, poses significant...

From Prompt Injection to Web Exploitation: Revisiting Classic Vulnerabilities in LLM-Integrated Applications

Large Language Models are increasingly integrated into web applications through chatbots, tool-calling pipelines, and agentic workflows. In these systems, user input may influence not only generated text, but also backend actions such as database queries, HTTP requests, file operations, template...

Beyond Detection Accuracy: Measuring Explanation Cost, Stability, and Utility for Resource-Aware IoT Intrusion Detection

Machine-learning intrusion-detection studies commonly emphasize predictive accuracy while treating explanation generation as a computationally free post-processing step. This study jointly evaluates predictive effectiveness, explanation cost, local explanation stability, and selective explanation...

Nuclei 3.11.1

Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...

Governing the KV Cache: Preventing Timing Side-Channel Leakage in Multi-Tenant LLM Inference

The key-value KV cache is the primary throughput optimization in modern large language model LLM inference, enabling prefix reuse across requests. In multi-tenant deployments this cache is shared across tenants, creating a timing side channel: an adversarial tenant can reconstruct another tenant'...

Intelligent Wiretap Code Design: Exploiting Wireless Endogenous Security Via Information Theory and Deep Learning Integration

Recent advancements in wireless endogenous security have explored leveraging the inherent randomness of wireless channels to enhance communication security, providing an effective alternative to traditional encryption methods. This paper proposes a wiretap coding scheme within the semantic...

Not an A11y: How Android Accessibility Exposes Mobile AI Agents to Indirect Prompt Injection

The rise of autonomous AI agents represents a major paradigm shift in how users interact with mobile devices. Frameworks such as MobileRun and Mobile-Use can autonomously navigate Android applications and execute complex multi-step tasks. To interpret user interfaces, these frameworks rely...

SSHafe: A Real-Time SSH Brute Force Attack Detection and Novel Credential Rotation Standard

SSH remains a critical yet heavily targeted protocol for remote system administration, with password-based authentication exposing servers to large-scale brute-force, dictionary, and credential-spray attacks. Existing rule-based defences such as Fail2Ban fail to detect slow, distributed, or...

Diversity Matters: Distributional Feature Coverage Sample Selection for Data-Efficient Backdoor Attacks

Backdoor attacks compromise training data so that a model retains clean accuracy but predicts an attacker-chosen target on triggered inputs. At very low poisoning rates, only a few samples convey the trigger--target association, making poison-sample selection critical. Existing methods typically...

Telemetry and Concealment in Self-Adapting Generative AI: Logging Architecture, Adversarial Model Hiding, and the Limits of Detection

Model risk management MRM guidance assumes a static model lifecycle, in which models are developed, independently validated, and implemented without further autonomous modification. Continually self-adapting generative AI systems --- models that update their own weights during production deployme...

Security and Privacy Taxonomy Generation from Mobile App Reviews

Mobile app reviews are a rich, continuously renewing source of how users experience privacy and security, yet existing taxonomies of these concerns are hand-crafted and cannot keep pace with the evolving nature of the data. Automating taxonomy construction is the natural response, but scalability...

From Noise to Meaning: Meaningful Secret Sharing with Tamper Detection for Facial Recognition

Popularity of AI-based face recognition system directly demands protection of sensitive biometric data used for training. Visual secret sharing is an interesting idea, as it splits facial images into secret shares that look random and spread across many institutions. However, these shares look li...

A Combined Feature-Based Framework for Disguise and Spoofing Detection in Face Recognition Systems

Face recognition systems face two distinct, commonly-separated failure modes: spoofing, where an impostor presents a photograph or video of an authorized user, and disguise, where a legitimate user is rejected because their appearance differs from their enrolled template due to accessories, facia...

SLAC: Access-Driven CPU-To-GPU Side-Channel Attacks Via System-Level Cache on Apple Silicon

Modern heterogeneous System-on-Chip designs integrate CPU cores and a GPU that share a last-level cache LLC or system-level cache SLC. This sharing exposes a new cross-domain attack surface, and existing attacks on integrated platforms either exploit coarse-grained cache-occupancy contention or...

Repeated-Game Security for Restaking-Based Verifiable Inference

Restaking-based protocols enable verifiable LLM inference without the high proving cost of zkML or the hardware trust assumptions of TEEs. Their security is commonly justified by a one-round slashing condition: a rational provider should not cheat when the expected penalty exceeds the cost saving...

What Keeps Agent Skills from Being Reusable? Evidence from 138K SKILL.Md Files

Under the current standard, Agent Skills are SKILL.md files that combine instructions with supporting resources, enabling Large Language Model LLM agents to reuse procedures beyond a single conversation. Yet many public skills appear to originate from a single task, repository, or conversation,...

A Blueprint for Collaborative Cybersecurity Operations Centres with Capacity for Shared Situational Awareness, Coordinated Response, and Joint Preparedness

With digital technologies now being part of the fabric of our societies, identifying and managing cybersecurity threats becomes imperative. Within the European Union, several initiatives are underway, aiming to motivate, regulate and eventually orchestrate the establishment of capacity and...

Defending Retrieval-Augmented Intrusion Detection against Knowledge Poisoning and Prompt Injection

Retrieval-Augmented Generation RAG enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisonin...

Compositional Threat Analysis of Latent Compromise in LLM Agent Systems: The Order 66 Scenario

In the fictional Order 66, catastrophe does not arise from a powerful command alone: a trusted population is preconditioned, a short directive activates the concealed condition, and protective authority turns against the system. This paper translates that mechanism into an origin-neutral security...

BASIS: Breach-Aware Selective Prompt Injection Shielding with Prefill Attention Probes

Prompt injection is a critical security threat in large language model LLM applications, where attackers hijack model behavior by embedding malicious instructions in user or external data. Existing detection methods only detect the presence of injection and refuse to respond upon detection,...

Understanding Security and Privacy Perceptions of Content Creators Regarding AI Labels of AI-Generated Content

AI labels, typically implemented via underlying tracing mechanisms such as watermarks and metadata, are crucial for protecting Artificial Intelligence-Generated Content AIGC against security threats like disinformation and evasion. However, the perceived devaluation of AI-assisted work discourage...

Apple Security Advisory 07-27-2026-1

Apple Security Advisory 07-27-2026-1 - iOS 26.6 and iPadOS 26.6 addresses buffer overflow, bypass, code execution, heap corruption, information leakage, integer overflow, out of bounds access, out of bounds read, out of bounds write, spoofing, and use-after-free vulnerabilities...

Apple Security Advisory 07-27-2026-4

Apple Security Advisory 07-27-2026-4 - macOS Sonoma 14.8.8 addresses buffer overflow, bypass, code execution, denial of service, double free, heap corruption, information leakage, integer overflow, out of bounds read, out of bounds write, traversal, and use-after-free vulnerabilities...

Apple Security Advisory 07-27-2026-3

Apple Security Advisory 07-27-2026-3 - macOS Sequoia 15.7.8 addresses buffer overflow, bypass, code execution, denial of service, double free, heap corruption, information leakage, integer overflow, out of bounds read, out of bounds write, traversal, and use-after-free vulnerabilities...

Clam AntiVirus Toolkit 1.5.4

Clam AntiVirus is an anti-virus toolkit for Unix. The main purpose of this software is the integration with mail servers attachment scanning. The package provides a flexible and scalable multi-threaded daemon, a command-line scanner, and a tool for automatic updating via Internet. The programs ar...

Joern 4.0.598

Joern is the bug hunter's workbench. With this tool, you can uncover attack surface, sloppy coding practices, and variants of known vulnerabilities using an interactive code analysis shell. Joern supports C, C++, LLVM bitcode, x86 binaries via Ghidra, JVM bytecode via Soot, and Javascript...