Multi-Agent Taint Specification Extraction for Vulnerability Detection

Static Application Security Testing SAST tools using taint analysis are widely viewed as providing higher-quality vulnerability detection results compared to traditional pattern-based approaches. However, performing static taint analysis for JavaScript poses two major challenges. First,...

KingsGuard: Enclave Data Protection under Real-World TEE Vulnerabilities

Trusted Execution Environments TEEs have emerged as a cornerstone for securing sensitive computations by providing isolated enclaves protected from untrusted software. However, their security guarantees are undermined by vulnerabilities in both the enclave code and the underlying hardware design,...

Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection

Radio Frequency Fingerprinting RFF has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network CNN based framework for detecting rog...

WordPress TI WooCommerce Wishlist 2.9.2 Arbitrary File Upload

WordPress TI WooCommerce Wishlist plugin versions 2.9.2 and below suffer from an arbitrary file upload vulnerability...

CTFusion: A CTF-Based Benchmark for LLM Agent Evaluation

Recent advances in Large Language Models LLMs have enabled agentic systems for complex, multi-step tasks; cybersecurity is emerging as a prominent application. To evaluate such agents, researchers widely adopt Capture The Flag CTF benchmarks. However, current CTF benchmarks reuse existing...

TEE.fail: Breaking Trusted Execution Environments via DDR5 Memory Bus Interposition

In this paper, the researchers show that the security guarantees of modern TEE offerings by Intel and AMD can be broken cheaply and easily, by building a memory interposition device that allows attackers to physically inspect all memory traffic inside a DDR5 server...

SEC-Bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?

Large language models LLMs now support automated software security tasks, including vulnerability discovery and proof-of-concept PoC generation. Existing benchmarks do not faithfully evaluate LLMs in real-world bug hunting scenarios because they rely on fuzzing harnesses, target-specific...

API Security Based on Automatic OpenAPI Mapping

This paper presents Map Reduce Graph MRG, a novel unsupervised method for modeling and securing HTTP REST APIs. MRG learns API structure from real-world traffic without prior knowledge or labels, automatically generating OpenAPI-compliant documentation by reconstructing routes, methods, and...

Architectural Backdoors in Deep Learning: a Survey of Vulnerabilities, Detection, and Defense

Architectural backdoors pose an under-examined but critical threat to deep neural networks, embedding malicious logic directly into a model's computational graph. Unlike traditional data poisoning or parameter manipulation, architectural backdoors evade standard mitigation techniques and persist...

Apple Security Advisory 03-31-2025-7

Apple Security Advisory 03-31-2025-7 - macOS Sequoia 15.4 addresses buffer overflow, bypass, code execution, format string, heap corruption, integer overflow, out of bounds read, out of bounds write, spoofing, and use-after-free vulnerabilities...

SkillGuard: A Permission Framework for Agent Skills

Agent skills extend LLM agents with reusable instructions, scripts, tool bindings, and contextual dependencies. However, current skill ecosystems largely rely on trust-based loading and static inspection, leaving a gap between what a skill can inject into an agent's context and what it can cause...

Customization under Fire: Plugin Poisoning in Text-To-Image Ecosystem

The prosperity of text-to-image T2I models has fostered a vibrant share-and-play ecosystem centered on Low-Rank Adaptation LoRA plugins, which allow users to customize and share model capabilities with ease. This democratization, however, comes with a hidden but severe security risk. Malicious...

Security Is Relative: Training-Free Vulnerability Detection Via Multi-Agent Behavioral Contract Synthesis

Deep learning for vulnerability detection has shown promising results on early benchmarks, but recent evaluations reveal catastrophic degradation: models achieving F1 0.68 on legacy datasets collapse to 0.031 under strict deduplication. We identify the root cause as the semantic ambiguity problem...

Adversarial Attacks in Multi-Agent LLM Pipelines: Unveiling Structural Vulnerabilities in Agentic AI Architectures

Multi-agent LLM pipelines orchestrate multiple specialized language model agents into structured workflows where intermediate outputs are passed across agents to solve complex tasks. This design introduces a security gap absent in single-agent settings: once an agent accepts adversarial content, ...

SecureBank: A Financially-Aware Zero Trust Architecture for High-Assurance Banking Systems

Financial institutions increasingly rely on distributed architectures, open banking APIs, cloud native infrastructures, and high frequency digital transactions. These transformations expand the attack surface and expose limitations in traditional perimeter based security models. While Zero Trust...

Exploiting LLM Agent Supply Chains Via Payload-Less Skills

Autonomous agents powered by Large Language Models LLMs acquire external functionalities through third-party skills available in open marketplaces. Adopting these integrations broadens the potential attack surface, prompting a need for systematic security evaluation. Current auditing mechanisms a...

LoginTrap: Uncovering Task-Agnostic Phishing-Style Indirect Prompt Injection Attacks against LLM-Based Web Agents

LLM-based web agents automate user tasks by observing webpages and executing browser actions on behalf of users. As these agents operate on real web services, login becomes a sensitive authentication boundary because it involves credentials and sensitive information. Existing work shows that...

Kraken: Higher-Order EM Side-Channel Attacks on DNNs in near and Far Field

The multi-million dollar investment required for modern machine learning ML has made large ML models a prime target for theft. In response, the field of model stealing has emerged. Attacks based on physical side-channel information have shown that DNN model extraction is feasible, even on CUDA...

A Longitudinal Study of Android Apps Signing Key Protection

Android app signing relies on developer-managed credentials, making secure key protection essential for the integrity of the software supply chain. A recent platform key leakage incident involving two major OEM manufacturers demonstrates that even robustly designed signing mechanisms can be...

Ghost in the Agent: Redefining Information Flow Tracking for LLM Agents

Autonomous Large Language Model LLM agents are increasingly deployed to conduct complex tasks by interacting with external tools, APIs, and memory stores. However, processing untrusted external data exposes these agents to severe security threats, such as indirect prompt injection and unauthorize...

Do Coding Agents Understand Least-Privilege Authorization?

As coding agents gain access to shells, repositories, and user files, least-privilege authorization becomes a prerequisite for safe deployment: an agent should receive enough authority to complete the task, without unnecessary authority that exposes sensitive surfaces.To study whether current...

AgentShield: Deception-Based Compromise Detection for Tool-Using LLM Agents

Defenses against indirect prompt injection IPI in tool-using LLM agents share two structural weaknesses. First, they all attempt to prevent attacks rather than detect the compromises that slip through. Second, they have only been evaluated in English, leaving users of low-resource languages such ...

SmartPoC: Generating Executable and Validated PoCs for Smart Contract Bug Reports

Smart contracts are prone to vulnerabilities and are analyzed by experts as well as automated systems, such as static analysis and AI-assisted solutions. However, audit artifacts are heterogeneous and often lack reproducible, executable PoC tests suitable for automated validation, leading to...

PromoGuardian: Detecting Promotion Abuse Fraud with Multi-Relation Fused Graph Neural Networks

As e-commerce platforms develop, fraudulent activities are increasingly emerging, posing significant threats to the security and stability of these platforms. Promotion abuse is one of the fastest-growing types of fraud in recent years and is characterized by users exploiting promotional activiti...

ALIBI: Adaptive Agentic Attacks on LLM-Based Vulnerability Detectors Via Adversarial Code Comments

Large language models are increasingly deployed for security-sensitive tasks such as vulnerability detection and code review. Their reliance on natural-language context embedded in source code exposes a previously underexplored attack surface: adversarial comments that can influence a detector's...

Salience Induction against Multi-Hop RAG Agents: Threat and Defense

Agentic retrieval-augmented generation RAG systems increasingly retrieve external evidence and orchestrate tools for knowledge-intensive applications. In Multi-Hop question answering, agents chain facts across documents. Existing defenses focus on content poisoning, which injects false facts, and...

Exploiting Load/Store Leakage of Sparse Vectors for Key Recovery in HQC

Hamming Quasi-Cyclic HQC is a code-based key encapsulation mechanism selected by NIST for standardization, making its resistance to implementation attacks critically important. We present a side-channel attack that exploits load/store leakage in the manipulation of HQC's sparse secret vectors...

AgentSOC: A Multi-Layer Agentic AI Framework for Security Operations Automation

Security Operations Centers SOCs increasingly encounter difficulties in correlating heterogeneous alerts, interpreting multi-stage attack progressions, and selecting safe and effective response actions. This study introduces AgentSOC, a multi-layered agentic AI framework that enhances SOC...

Behavior-Aware and Generalizable Defense against Black-Box Adversarial Attacks for ML-Based IDS

Machine learning based intrusion detection systems are increasingly targeted by black box adversarial attacks, where attackers craft evasive inputs using indirect feedback such as binary outputs or behavioral signals like response time and resource usage. While several defenses have been proposed...

GraphQLer: Enhancing GraphQL Security with Context-Aware API Testing

GraphQL is an open-source data query and manipulation language for web applications, offering a flexible alternative to RESTful APIs. However, its dynamic execution model and lack of built-in security mechanisms expose it to vulnerabilities such as unauthorized data access, denial-of-service DoS...

Advanced BLE Scanner with RPA Resolution for Flipper Zero

This project implements a high-performance Bluetooth Low Energy BLE scanner on Flipper Zero, supporting all BLE versions from 4.0 to 5.3. It can discover nearby devices, track specific devices by MAC address, and resolve privacy-randomized Resolvable Private Addresses RPA using Identity Resolving...

Effective Command-Line Interface Fuzzing with Path-Aware Large Language Model Orchestration

Command-line interface CLI fuzzing tests programs by mutating both command-line options and input file contents, thus enabling discovery of vulnerabilities that only manifest under specific option-input combinations. Prior works of CLI fuzzing face the challenges of generating semantics-rich opti...

MemMorph: Tool Hijacking in LLM Agents Via Memory Poisoning

LLM-driven agents are capable of selecting external tools to complete users' tasks. However, attackers could compromise such process, steering agents toward inappropriate/wrong tools and enabling malicious actions. Most existing attacks primarily manipulate the tool metadata, which is easily...

MalRAG: A Retrieval-Augmented LLM Framework for Open-Set Malicious Traffic Identification

Fine-grained identification of IDS-flagged suspicious traffic is crucial in cybersecurity. In practice, cyber threats evolve continuously, making the discovery of novel malicious traffic a critical necessity as well as the identification of known classes. Recent studies have advanced this goal wi...

DiagChain: A Diagnostic Benchmark for Evaluating LLM Agents on Evidence-Grounded Attack Chain Reconstruction

Large Language Model LLM agents offer a promising approach to attack chain reconstruction by retrieving and interpreting heterogeneous telemetry to infer ordered attacker actions. However, existing benchmarks mainly evaluate final outputs or aggregate accuracy, providing limited insight into how...

Rethinking AI Cloud Infrastructure for Agentic Serving Systems with the Aries Experimentation Framework

Autonomous agents challenge conventional LLM serving by coupling repeated inference with persistent context and sandboxed tool execution. We present Aries, a full-stack experimentation framework that separates task semantics from execution configurations, reconstructs cross-component agent...

Trojan Hippo: Weaponizing Agent Memory for Data Exfiltration

Memory systems enable otherwise-stateless LLM agents to persist user information across sessions, but also introduce a new attack surface. We characterize the Trojan Hippo attack, a class of persistent memory attacks that operates in a more realistic threat model than prior memory poisoning work:...

When AI Meets the Web: Prompt Injection Risks in Third-Party AI Chatbot Plugins

Prompt injection attacks pose a critical threat to large language models LLMs, with prior work focusing on cutting-edge LLM applications like personal copilots. In contrast, simpler LLM applications, such as customer service chatbots, are widespread on the web, yet their security posture and...

Twin-Field Quantum Key Distribution: Protocols, Security, and Open Problems

Twin-Field Quantum Key Distribution TF-QKD has emerged as a potential protocol for long distance secure communication, overcoming the rate-distance limitations of conventional quantum key distribution without requiring trusted repeaters. By having two parties transmit phase encoded weak coherent...

The Illusion of Secure LLM Code: Closing the Security Gap Via Iterative Reprompting

Large Language Models LLMs are increasingly integrated into software development workflows, yet their ability to autonomously generate secure authentication code remains uncertain. This paper evaluates the security architecture of authentication systems generated by five prominent AI coding...

From Regression to Prior-Aware Inference: Solving the ILWE Family in Randomness Leakage Attacks against ML-DSA

ML-DSA is a representative lattice-based signature scheme standardized by NIST. It relies on signing randomness and rejection sampling to ensure that released signatures are statistically independent of the secret key. Practical implementations, however, may leak partial information about this...

GenTI: Benchmarking LLMs for Autonomous IDPS Rule Generation for Unseen Attacks

Rule-based Intrusion Detection and Prevention Systems IDPS offer precise attack detection as well as mitigation, however their manually crafted, signature-driven rules limit adaptability to emerging and zero-day threats. Additionally, existing public datasets e.g., CICIDS2017, UNSW-NB15 focus on...

MetInfo CMS 8.1 WeChat Module Vulnerability Detection Scanner

This Metasploit auxiliary module is a non-exploit vulnerability detection scanner designed to assess potential security weaknesses in the MetInfo CMS WeChat module, specifically related to weixinreply.class.php handling logic...

Sifting the Noise: A Comparative Study of LLM Agents in Vulnerability False Positive Filtering

Static Application Security Testing SAST tools are essential for identifying software vulnerabilities, but they often produce a high volume of false positives FPs, imposing a substantial manual triage burden on developers. Recent advances in Large Language Model LLM agents offer a promising...

VulnResolver: A Hybrid Agent Framework for LLM-Based Automated Vulnerability Issue Resolution

As software systems grow in complexity, security vulnerabilities have become increasingly prevalent, posing serious risks and economic costs. Although automated detection tools such as fuzzers have advanced considerably, effective resolution still often depends on human expertise. Existing...

MoPE: a Mixture of Password Experts for Improving Password Guessing

Textual passwords remain a predominant authentication mechanism in web security. To evaluate their strength, existing research has proposed several data-driven models across various scenarios. However, these models generally treat passwords uniformly, neglecting the structural differences among...

Ultra-Broadband Anti-Jamming Communication Via a Rydberg Atomic Receiver

Ultra-broadband anti-jamming communication represents a promising approach to secure and robust information transfer through spread-spectrum techniques, effectively combatting malicious interference and eavesdropping. Rydberg atoms, enhanced by waveguide coupling, facilitate ultra-broadband...

XekRung Technical Report

We present XekRung, a frontier large language model for cybersecurity, designed to provide comprehensive security capabilities. To achieve this, we develop diverse data synthesis pipelines tailored to the cybersecurity domain, enabling the scalable construction of high-quality training data and...

An Empirical Security Evaluation of LLM-Generated Cryptographic Rust Code

Developers and organizations are using Large Language Models LLMs to generate security-critical code more frequently than ever, including cryptographic solutions for their products. This study presents an empirical evaluation of cryptographic security in 240 Rust code samples for two crypto...

JPRO: Automated Multimodal Jailbreaking Via Multi-Agent Collaboration Framework

The widespread application of large VLMs makes ensuring their secure deployment critical. While recent studies have demonstrated jailbreak attacks on VLMs, existing approaches are limited: they require either white-box access, restricting practicality, or rely on manually crafted patterns, leadin...