TGL-APT: Temporal Graph Learning with Graph Distillation for Efficient APT Investigation

Advanced Persistent Threat APT attacks pose a critical challenge to modern systems, as their stealthy, multi-stage nature renders conventional detection methods ineffective. While provenance graphs provide rich behavioral context for attack investigation, attack-relevant evidence is often sparse...

SparSEEty: Extracting Tokens from Sparsity-Exploiting LLM Serving Systems Via Deterministic Side Channels

Modern large language models LLMs exhibit activation sparsity, wherein only a subset of their neurons is activated for given input tokens. Researchers have leveraged this property to optimize LLM serving systems by omitting weight accesses and computations pertaining to inactive neurons...

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...

Free-Riding in the AI Economy: Demystifying Logic Flaws in X402-Enabled Payment Systems

The agentic economy demands programmatic financial rails, positioning the x402 protocol as the de facto standard for machine-to-machine payments. However, bridging synchronous HTTP requests with asynchronous blockchain finality introduces profound state synchronization challenges. In this work, w...

Context-Aware Web Attack Detection in Open-Source SIEM Systems Via MITRE ATT&CK-Enriched Behavioral Profiling

Security Information and Event Management SIEM systems aggregate log data from heterogeneous sources to detect coordinated attacks. Traditional rule-based correlation engines struggle to classify multi-step web application attacks because they examine each event without reference to the behaviour...

From Similarity to Vulnerability: Key Collision Attack on LLM Semantic Caching

Semantic caching has emerged as a pivotal technique for scaling LLM applications, widely adopted by major providers including AWS and Microsoft. By utilizing semantic embedding vectors as cache keys, this mechanism effectively minimizes latency and redundant computation for semantically similar...

A Systematic Study of Code Obfuscation against LLM-Based Vulnerability Detection

As large language models LLMs are increasingly adopted for code vulnerability detection, their reliability and robustness across diverse vulnerability types have become a pressing concern. In traditional adversarial settings, code obfuscation has long been used as a general strategy to bypass...

Time Will Tell: Large-Scale De-Anonymization of Hidden I2P Services Via Live Behavior Alignment (Extended Version)

I2P Invisible Internet Project is a popular anonymous communication network. While existing de-anonymization methods for I2P focus on identifying potential traffic patterns of target hidden services among extensive network traffic, they often fail to scale effectively across the large and diverse...

Inside LockBit: Technical, Behavioral, and Financial Anatomy of a Ransomware Empire

LockBit has evolved from an obscure Ransomware-as-a-Service newcomer in 2019 to the most prolific ransomware franchise of 2024. Leveraging a recently leaked MySQL dump of the gang's management panel, this study offers an end-to-end reconstruction of LockBit's technical, behavioral, and financial...

Insecurity through Obscurity: Veiled Vulnerabilities in Closed-Source Contracts

Most blockchains cannot hide the binary code of programs i.e., smart contracts running on them. To conceal proprietary business logic and to potentially deter attacks, many smart contracts are closed-source and employ layers of obfuscation. However, we demonstrate that such obfuscation can obscur...

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...

(A)I Sees What You Don't: Exploiting New Attack Surfaces in Third-Party Mobile Agents

Third-party mobile agents powered by Vision-Language Models VLMs have emerged as a promising paradigm for automating smartphone interactions. These agents act as high-privilege decision-makers, perceiving device states through screenshots and executing actions via VLM reasoning, transforming how ...

Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning

We define Oracle Poisoning, an attack class in which an adversary corrupts a structured knowledge graph that AI agents query at runtime via tool-use protocols, causing incorrect conclusions through correct reasoning. Unlike prompt injection, Oracle Poisoning manipulates the data agents reason ove...

Longitudinal Analyses of SAST Tools: A CodeQL Case Study

Open-source software OSS pipelines rely on automated static analysis tools to prevent the introduction of vulnerabilities in code. However, there is limited understanding of the efficacy of these tools across the OSS ecosystem over time. In this paper, we introduce a novel method to evaluate stat...

PISmith: Reinforcement Learning-Based Red Teaming for Prompt Injection Defenses

Prompt injection poses serious security risks to real-world LLM applications, particularly autonomous agents. Although many defenses have been proposed, their robustness against adaptive attacks remains insufficiently evaluated, potentially creating a false sense of security. In this work, we...

Securing AI Agents against Prompt Injection Attacks

Retrieval-augmented generation RAG systems have become widely used for enhancing large language model capabilities, but they introduce significant security vulnerabilities through prompt injection attacks. We present a comprehensive benchmark for evaluating prompt injection risks in RAG-enabled A...

Red Hat Security Advisory 2026-55757-03

Red Hat Security Advisory 2026-55757-03 - An update for the 389-ds:1.4 module is now available for Red Hat Enterprise Linux 8.4 Advanced Mission Critical Update Support and Red Hat Enterprise Linux 8.4 Extended Update Support Long-Life Add-On. Issues addressed include buffer overflow and null...

MalTotal: Cost-Effective and Language-Agnostic Malicious Code Poisoning Detection for Millions of Repositories

The widespread adoption of open source software OSS has introduced significant security risks, with malicious code poisoning attacks increasingly targeting public package registries and open-source platforms. Existing detection approaches, including heuristic-, learning-, and LLM-based methods,...

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...

PTSan: A Practical Memory Safety Sanitizer for C/C++ with Pointer-Object Authority

Memory safety errors remain the dominant source of severe vulnerabilities in C and C++. Pointer-based sanitizers provide stronger guarantees than location-based tools such as LLVM's ASan, but their overhead and compatibility limitations have constrained production use. We present PTSan, an LLVM...

Shared Vulnerabilities in Robustness-Optimized Defenses: One Breach Exposes the Family

Adversarial robustness optimization aims to preserve correct prediction under adversarial perturbations, and has produced substantial robustness gains through methods such as adversarial training and adversarial purification. However, we identify a new security risk: these gains can create shared...

DSA Nonce Vulnerabilities: An Interactive Analysis

Digital signatures are fundamental to identity authentication and data integrity in cybersecurity, and the NIST-standardized Digital Signature Algorithm DSA frequently appears in the cryptography track of CTF competitions. However, DSA relies on number theory, modular arithmetic, and large-intege...

MRMMIA: Membership Inference Attacks on Memory in Chat Agents

Membership inference attacks MIAs test whether a target data record belongs to a system's private data, and have become a standard tool to measure privacy leakage in machine learning systems. Prior work has primarily focused on training corpora or retrieval databases. However, MIAs against agent...

GPUBreach: Privilege Escalation Attacks on GPUs Using Rowhammer

NVIDIA GPUs with GDDR memories have been shown susceptible to Rowhammer-based bit-flips, similar to CPUs. However, Rowhammer exploits on GPUs have been limited to injecting untargeted bit-flips in victim data like weights of machine learning models, to degrade model accuracy, unlike CPU exploits...

LLM4CodeRE: Generative AI for Code Decompilation Analysis and Reverse Engineering

Code decompilation analysis is a fundamental yet challenging task in malware reverse engineering, particularly due to the pervasive use of sophisticated obfuscation techniques. Although recent large language models LLMs have shown promise in translating low-level representations into high-level...

ThinkTrap: Denial-Of-Service Attacks against Black-Box LLM Services Via Infinite Thinking

Large Language Models LLMs have become foundational components in a wide range of applications, including natural language understanding and generation, embodied intelligence, and scientific discovery. As their computational requirements continue to grow, these models are increasingly deployed as...

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...

Adversarial Sample Generation for Anomaly Detection in Industrial Control Systems

Machine learning ML-based intrusion detection systems IDS are vulnerable to adversarial attacks. It is crucial for an IDS to learn to recognize adversarial examples before malicious entities exploit them. In this paper, we generated adversarial samples using the Jacobian Saliency Map Attack JSMA...

Metabase Authentication Bypass / SQL Injection

Metabase versions x.58.0 through x.63.4 contain an unauthenticated SQL injection vulnerability in the password reset functionality that can allow a remote attacker to manipulate database queries and obtain administrator access. This proof of concept exploits the vulnerable password reset endpoint...

Geometric Configurations of Perturbed Jailbreak Prompts

Perturbation techniques that turn unsuccessful jailbreak prompts into successful ones are continuously evolving, constituting a major security threat to LLM safety. In this paper, we investigate the internal representations of such string-level perturbed jailbreak inputs in the small weight model...

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...

When the Abyss Looks Back: Unveiling Evolving Dark Patterns in Cookie Consent Banners

To comply with data protection regulations such as the EU General Data Protection Regulation GDPR and the California Consumer Privacy Act CCPA, websites widely deploy cookie consent banners to collect users' privacy preferences. In practice, however, these interfaces often embed dark patterns tha...

When Handshakes Tell the Truth: Detecting Web Bad Bots Via TLS Fingerprints

Automated traffic continued to surpass human-generated traffic on the web, and a rising proportion of this automation was explicitly malicious. Evasive bots could pretend to be real users, even solve Captchas and mimic human interaction patterns. This work explores a less intrusive, protocol-leve...

HogVul: Black-Box Adversarial Code Generation Framework against LM-Based Vulnerability Detectors

Recent advances in software vulnerability detection have been driven by Language Model LM-based approaches. However, these models remain vulnerable to adversarial attacks that exploit lexical and syntax perturbations, allowing critical flaws to evade detection. Existing black-box attacks on...

The Art of Hide and Seek: Making Pickle-Based Model Supply Chain Poisoning Stealthy Again

Pickle deserialization vulnerabilities have persisted throughout Python's history, remaining widely recognized yet unresolved. Due to its ability to transparently save and restore complex objects into byte streams, many AI/ML frameworks continue to adopt pickle as the model serialization protocol...

BACFuzz: Exposing the Silence on Broken Access Control Vulnerabilities in Web Applications

Broken Access Control BAC remains one of the most critical and widespread vulnerabilities in web applications, allowing attackers to access unauthorized resources or perform privileged actions. Despite its severity, BAC is underexplored in automated testing due to key challenges: the lack of...

Apple Security Advisory 05-11-2026-1

Apple Security Advisory 05-11-2026-1 - iOS 26.5 and iPadOS 26.5 addresses buffer overflow, bypass, denial of service, information leakage, null pointer, out of bounds access, out of bounds read, out of bounds write, and use-after-free vulnerabilities...

ADR: An Agentic Detection System for Enterprise Agentic AI Security

We present the Agentic AI Detection and Response ADR system, the first large-scale, production-proven enterprise framework for securing AI agents operating through the Model Context Protocol MCP. We identify three persistent challenges in this domain: 1 limited observability -- existing Endpoint...

AgentRFC: Security Design Principles and Conformance Testing for Agent Protocols

AI agent protocols -- including MCP, A2A, ANP, and ACP -- enable autonomous agents to discover capabilities, delegate tasks, and compose services across trust boundaries. Despite massive deployment MCP alone has 97M+ monthly SDK downloads, no systematic security framework for these protocols...

LLMs As Firmware Experts: A Runtime-Grown Tree-Of-Agents Framework

Large Language Models LLMs and their agent systems have recently demonstrated strong potential in automating code reasoning and vulnerability detection. However, when applied to large-scale firmware, their performance degrades due to the binary nature of firmware, complex dependency structures, a...

BLADE: Behavior-Level Anomaly Detection Using Network Traffic in Web Services

With their widespread popularity, web services have become the main targets of various cyberattacks. Existing traffic anomaly detection approaches focus on flow-level attacks, yet fail to recognize behavior-level attacks, which appear benign in individual flows but reveal malicious purpose using...

AgentCyTE: Leveraging Agentic AI to Generate Cybersecurity Training and Experimentation Scenarios

Designing realistic and adaptive networked threat scenarios remains a core challenge in cybersecurity research and training, still requiring substantial manual effort. While large language models LLMs show promise for automated synthesis, unconstrained generation often yields configurations that...

Apple Security Advisory 05-12-2025-3

Apple Security Advisory 05-12-2025-3 - macOS Sequoia 15.5 addresses bypass, code execution, double free, information leakage, integer overflow, out of bounds read, and use-after-free vulnerabilities...

CISA: A Tale of Two SOCs - Insights from Two Red Team Assessments

The Cybersecurity and Infrastructure Security Agency CISA conducted simultaneous red team assessments at two organizations and observed different defensive outcomes. In both environments, the red team achieved full domain compromise and accessed sensitive business systems SBSs and cloud resources...

SEVRA-BENCH: Social Engineering of Vulnerabilities in Review Agents

Large language model LLM reviewers are increasingly used in pull-request PR workflows, where their approvals help decide which code is merged into a repository. This raises a question that benchmarks for static vulnerability detection or code generation do not address: can an automated reviewer...

Persona Attack: Incremental Memory Injection Jailbreak Attack against Large Language Models

As Large Language Models evolve for user convenience, vulnerability to jailbreak attacks continues to be reported despite ongoing efforts in safety training. Traditional jailbreak techniques typically focus on a single prompt injection, neglecting the models' ability to remember the flow of...

Refusal Before Decoding: Detecting and Exploiting Refusal Signals in Intermediate LLM Activations

In this paper, we investigate whether refusal behavior can be predicted from LLM intermediate activations before decoding using linear probes trained on residual stream activations at each transformer block. We find that refusal is linearly decodable well before the final layer, indicating that...

The Attack and Defense Landscape of Agentic AI: A Comprehensive Survey

AI agents that combine large language models with non-AI system components are rapidly emerging in real-world applications, offering unprecedented automation and flexibility. However, this unprecedented flexibility introduces complex security challenges fundamentally different from those in...

Breaking Bad Email Habits: Bounding the Impact of Simulated Phishing Campaigns

Simulated phishing campaigns are widely deployed, yet the behavioral data they produce is endogenous: because training is triggered by clicking, the employees receiving intervention have already demonstrated susceptibility. This endogeneity, combined with the difficulty of separating genuine habi...

Adaptive Detection of Polymorphic Malware: Leveraging Mutation Engines and YARA Rules for Enhanced Security

Polymorphic malware continually alters its structure to evade signature-based defences, challenging both commercial antivirus AV and enterprise detection systems. This study introduces a reproducible framework for analysing eight polymorphic behaviours-junk code insertion, control-flow obfuscatio...