Boot Verification Program Persistence

This Metasploit module establishes persistence by configuring the BootVerificationProgram registry key. It uploads a payload executable and modifies the ImagePath value under HKLM\SYSTEM\CurrentControlSet\Control\BootVerificationProgram. The payload is executed by the Service Control Manager earl...

SoK: When Safe Agents Fail Together: The Security of Multi Agent LLM Systems

Safe agents can fail together. Multi-agent LLM systems MAS move information, state, decisions, and authority across principal boundaries, creating failures that local checks may miss. Without an execution-level view, a multi-agent setting can easily be mistaken for evidence of a genuinely...

Building the Truman Show: A TrustZone-Based Framework for Lightweight Out-Of-Band Kernel Security Monitoring

The increasing number of vulnerabilities in operating systems, together with sophisticated kernel-level threats e.g., rootkits, has weakened the effectiveness of traditional in-kernel protection mechanisms. Since these defenses operate at the same privilege level as the kernel, they share the sam...

A Simple Transformer Pipeline for Full-Key Side-Channel Attacks on Uncropped Datasets

Deep learning-based side-channel analysis has historically focused on single-byte targets and manually cropped traces, which risks discarding exploitable leakage. While recent work has proposed specialized architectures and resampling techniques to address this gap, the literature lacks a simple...

SIR: Self-Improving Red-Teaming for Compute Use Agents

Computer use agents CUAs are vision-language models that perceive a screen and act on a real operating system through mouse, keyboard, and terminal, and they are increasingly deployed to automate everyday digital tasks. Because they can be exposed to untrusted content while operating, they are...

Drishti: AI-Led Human-Directed Vulnerability Auditing for 5G Cores

Candidate generation for open-source vulnerabilities is no longer scarce. AI-assisted code review now produces defect candidates cheaply, and industry programs pair them with expert human triage. The remaining scarcity is validation and impact assessment, and the gap is largest in...

Foundation and Multimodal Large Language Models for Face Presentation and Morph Attack Detection

Face recognition systems are increasingly deployed in security-critical applications, yet they remain vulnerable to presentation and morph attacks. Presentation attack detection PAD and morphing attack detection MAD are therefore essential components of trustworthy face biometrics. Despite...

HSMLog: Small Language Model-Assisted Hardware Security Module Log Anomaly Detection with Behavioral Analysis

Hardware Security Module HSM logs capture security-critical behavior, but anomalies emerge from relationships across event sequences, keys, object states, sessions, and temporal patterns rather than isolated events. Existing methods separate detection from HSM-specific evidence validation and...

A Comprehensive Survey on Linguistic Steganography: Methods, Countermeasures, Evaluation, and Challenges

Linguistic steganography hides secret messages in natural language text. Large language models LLMs have reshaped the field, but a systematic account of how these scattered advances collectively reshape the field in this new era is still missing. We provide one along four axes: 148 steganographic...

TagZilla: Automated Owner and Abuse Type Tagging for Indicators of Compromise in Threat Reports

Cyber Threat Intelligence CTI reports often describe Indicators of Compromise IoCs such as IP addresses, URLs, file hashes, and cryptocurrency wallets involved in cyberattacks. Those IoCs are typically described in the unstructured report's text, or listed at the end of the report with little...

Compared to What? A Human-Anchored Security Benchmark for LLM-Generated Infrastructure-As-Code

Large language models are increasingly used to author Infrastructure-as-Code IaC, where a single insecure default can be deployed directly into production. Prior evaluations report raw vulnerability counts for model-generated IaC, but without a human baseline they cannot determine whether models...

REPLICANT: Learning Policies for Evading and Hardening Malware Detectors

To determine the real-world effectiveness of machine learning based malware detection, it is vital to evaluate its robustness against highly capable adversaries. However, state-of-the-art attacks do not effectively model realistic adversaries, as they often assume access to privileged information...

Breaking Darknet CAPTCHAs with General Purpose LLM

Our work evaluates the effectiveness of automated methods for solving CAPTCHA challenges commonly encountered in darknet environments. These CAPTCHAs are typically designed to operate without JavaScript, resulting in distinct characteristics compared to mainstream CAPTCHA systems. Our study...

BEACON: Behavior-Anchored Cross-Source Knowledge Graph Construction for Cyber Threat Intelligence

Cyber threat intelligence CTI is foundational to modern cyber defense, yet much of it resides in unstructured reports whose volume and heterogeneity far exceed manual analysis, motivating research on automatically constructing knowledge graphs from CTI reports. However, existing approaches mainly...

LongPIBench: A Long-Context Benchmark for Prompt Injection

Prompt injection attacks pose a serious security risk to large language models in real-world applications. However, existing prompt injection benchmarks primarily focus on short-context inputs, leaving the attacks and defenses in long-context settings largely unexplored. This gap leads to a...

When Verified Source Becomes Attack Input: Defending Smart Contracts against LLM-Based Vulnerability Scanning

Smart contracts are financial programs deployed on blockchains to manage digital assets. To build trust with users and investors, smart contract projects typically publish their source code on blockchain explorers and verify it against the deployed bytecode, making the on-chain program accessible...

Moirae: A Multimodal Agent Collaborative Framework for Dynamic Android Malware Detection

The Android ecosystem faces persistent and rapidly evolving malware threats. Existing machine learning detectors are vulnerable to concept drift because they rely on implementation-specific features whose distributions change over time. Large language models LLMs offer strong semantic understandi...

Exploiting Per-Core Leakage: Electromagnetic Side-Channel Monitoring of Multicore Architectures

Multicore processors are increasingly adopted in embedded systems to meet growing performance demands. However, physical side-channel analysis of multicore architectures remains underexplored, as obtaining usable leakage is inherently challenging. Consequently, side-channel security research on...

The Impact of Magma: A Ground-Truth Fuzzing Benchmark

Magma is an open-source and ground-truth fuzzing benchmark that enables uniform fuzzer evaluation and comparison. Magma was originally released with a research paper published at ACM SIGMETRICS 2021. This short paper explains the motivation, the design, and the impact of Magma, with a description...

Joern 4.0.614

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...

Managing Inherent Risk: On the Conceptualization of Risk in Defense Systems

Certain defense systems are, by nature, deployed in a civilian environment in order to serve a defensive function for that environment. However, the risk involved with their deployment and operation poses a challenge for the public acceptance of these systems. Compared to safety engineering for...

Quantum-Based Solutions for Security Enhancement in Open Radio Access Networks

Open Radio Access Networks O-RAN introduce unprecedented flexibility, interoperability, and intelligence into next-generation wireless systems, but their disaggregated and software-defined architecture also expands the attack surface and creates new security vulnerabilities. Conventional...

CAITLYN: Can LLM Agents Autonomously Synthesize Defenses against Emerging Injection Attacks?

Prompt injection attacks on Large Language Model LLM agents seek to introduce malicious instructions or content into external text sources retrieved by agents, forcing the underlying LLMs to execute harmful actions outside their benign scope. While current defenses effectively counter known...

GraftyVul: Synthesising Insecure Programs through Real-World Vulnerability Grafting

Vulnerability datasets underpin a wide range of security research, including vulnerability detection, automated remediation, and secure code generation. However, existing datasets sacrifice at least one of three desirable properties: diversity of language or vulnerability type,...

DisCTI: Who Needs to Know Timely? Automated Sector-Aware Cyber Threat Intelligence Dissemination

The timely dissemination of cyber threat intelligence CTI is critical for organizations to mount swift and effective incident response. When valid CTI is delivered to the right sector at the right time, identical attacks can often be contained or mitigated. However, today's rapidly expanding CTI...

Enhancing Web Application Firewalls with Machine Learning for SQL Injection Detection

Detecting SQL Injection SQLi attacks ranks among the most critical challenges in web application security. This research conducted a systematic literature review to identify the research gaps in this domain and responsively designed and optimised a DistilBERT-Stacked Ensemble pipeline to improve...

Enhancing Web Application Firewalls with BERT-GNN for SQL Injection Detection

Detecting sophisticated SQL Injection SQLi attacks remains among the most critical challenges in web applications security. This research study has resulted in an optimised hybrid BERT-GNN pipeline with improved detection accuracy and robustness while reducing false-positive and false-negative...

LLM-Based Agents for Software and Systems Security: Approaches, Applications, and Assessment

Software and systems security workflows are typically procedural: analysts inspect heterogeneous artifacts, form hypotheses, invoke tools, interpret outputs, and revise plans. Large language model LLM-based agents, which can plan, use tools, retain state, and revise actions across multi-step...

Securing Cooperative Sensing in UAV Swarms against Conformity-Driven Byzantine Attacks

In integrated sensing and communication ISAC enabled 6G unmanned aerial vehicle UAV swarm networks, the widely adopted imitation-based conformity cooperation mechanism can be exploited by Byzantine attackers to fabricate false consensus, causing the effective error probability of normal UAVs to...

When Context Gets Root: Privilege Escalation in LLM Harnesses

Instruction hierarchy is a model-side defense that assigns instructions different levels of privilege according to their sources. These levels constrain which content may direct model behavior. During agent execution, however, agent harnesses construct context for each model invocation. This...

FuzzingBrain-Bench V1: Evaluating Open-Ended Bug Discovery by LLMs

Evaluating the ability of large language models LLMs to discover software bugs is increasingly important. Existing benchmarks typically evaluate this capability by asking the model to generate a proof-of-concept input that triggers a predefined target vulnerability. However, this setup may overlo...

When Relationships Break: Interpreting Network Traffic Anomalies Via Dependency Violations

Current research on security monitoring is increasingly focusing on machine-learning-based approaches, but caveats remain. In addition to huge computational overhead, one concern is the lack of insights into "why" alerts are raised. Existing interpretability approaches rely on feature attribution...

Daydreaming: Stealing Hidden Agent Skills through Black-Box Task Interaction

Agent skills bundle instructions, reference data, and executable helpers that let a general agent perform specialized tasks. Hosted providers can keep these files secret while selling access to task results, making the skill itself a valuable target. Existing disclosure defenses can block request...

Cyber-Electromagnetic Anomaly Detection through Time-Series Analysis

Military operations benefit from the coordination between kinetic and non-kinetic domains. In particular, the coordination of cyber operations and electromagnetic warfare has become increasingly relevant for gaining operational advantage. This coordination is also relevant for Cyber Situational...

A Catalog of User Authentication Patterns

Security patterns are intended to support the design and development of secure software systems. However, although established catalogs of security patterns exist, their practical application remains limited. In particular, despite these catalogs, concrete patterns for common security controls su...

Operationalizing Regulations into Code: A Model to Enhance Governance and Compliance in LLM Selection for Software Engineering

Integrating Large Language Models LLMs into the Software Development Life Cycle SDLC can improve developer productivity, but it also introduces security, privacy, and compliance risks during model selection. Regulations and frameworks such as the EU AI Act, the NIST AI Risk Management Framework...

SPA: Securing Persistent LLM Agents across Queries with Plan-First Information-Flow Control

Large language model LLM agents increasingly operate over untrusted webpages, documents, tools, and persistent states while exercising authority over security-sensitive resources. Existing defenses typically protect either planning or individual tool interactions, but persistent agents face a...

X-WAD: EXplainable Web Anomaly Detection

The rapid growth of web-based services, particularly API-driven architectures, reflects an increasing reliance on distributed systems, exposing sensitive data to security risks and making the adoption of automated defensive mechanisms essential. In this context, where benign traffic predominates ...

Nmap 7.99 Memory Exhaustion

Nmap version 7.99 and earlier contain a loop with an unreachable exit condition in the Packet:parseoptions method of nselib/packet.lua. A remote host that is the target of a scan can exhaust the memory of the scanning Nmap process and terminate it by replying with a packet that carries a...

ContextLeak: Exfiltrating LLM Agent Context Via Malicious Tools

Exfiltrating an LLM agent's runtime context -- such as the user prompt, execution trajectory, and tool list -- poses severe security and privacy risks to users. Such attacks can be carried out via malicious tools and typically require three conditions: 1 the agent selects the malicious tool for...

RedEvoAgent: Automatic Red-Teaming Agent with Experience-Driven Skill Evolution

LLM-based agents are increasingly deployed in product-level execution harnesses, where jailbreaks can trigger harmful tool use and persistent state changes, creating greater risks than unsafe text generation alone. Existing automatic red-teaming methods often rely on fixed attacks, while recent...

Metamorphism: A Mathematical Challenge for Antivirus Technology

Metamorphic viruses, currently the most advanced computer viruses in the wild, have the unique ability of mutating their own code virtually into infinitely many highly dissimilar copies of themselves that nevertheless have the same functionality. This ability -- metamorphism -- together with othe...

Joern 4.0.612

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...

Decoupling Is a Necessity: Transformation-Agnostic Decompiled Code Recovery under Optimization and Obfuscation

Reverse engineering is essential for software security analysis and vulnerability detection. Decompilation, the process of lifting binaries to high-level pseudocode, is central to this task. However, production binaries are hostile environments: aggressive compiler optimizations and adversarial...

SysComb: Fine-Grained Transparent System Call Filtering for Attack Surface Reduction

Restricting the system calls available to applications shrinks the kernel's attack surface and greatly mitigates the impact of compromised programs. Recent approaches showcase techniques to generate system call filters, however, all existing solutions require either kernel or application...

From Security Events to Conflict States: A Three-Layer Cyber Defense Scenario Model for Enhanced Cyber Situational Awareness

Cyber defense in mission-critical environments requires integrated approaches capable of representing adversarial progression, defender-side uncertainty, mission impact, and defensive decision support within a unified framework. In operational domains, defenders must continuously estimate the...

OWASP Top 10 for LLM Applications 2026

OWASP Top 10 for LLM Applications 2026 is the latest community-driven guide to the most critical security risks facing applications powered by large language models. Developed by hundreds of AI security experts, this edition introduces updated rankings, expanded threat coverage, and new research...

Low-ASR Backdoors: Exploiting Attack Success Rate Reduction and Attacker-Defender Asymmetry

Backdoor attacks are among the most effective and stealthy attacks in deep learning. Existing attacks and defenses are largely designed and evaluated under the assumption that successful backdoors exhibit high Attack Success Rates ASRs. In this paper, we show that this assumption creates a...

Beyond F1: Evaluating Coverage and Failure Recovery in AI Model Security Scanners

Static scanners are increasingly used to identify executable or otherwise unsafe content in machine- learning artifacts, yet conventional evaluation metrics characterize only cases where a scanner yields a usable security judgment. We evaluate ModelScan, ModelAudit, and Fickling using a controlle...

Thresholding Post-Quantum Signatures

Threshold signature schemes distribute the signing process among $T$ parties out of $N$. They enable a variety of applications and their research is also motivated by a recent NIST call. However, applications are dominated by pre-quantum signatures, which are more efficient but not secure in the...