Black Hat Asia 2026 | Exploiting BLE Re-Pairing with the BLERP Attacks

Bluetooth Low Energy (BLE) security relies on a Long-Term Key (LTK) that serves as a root of trust. Users implicitly trust their paired devices, such as laptops, mice, and keyboards, assuming that once paired, they are secure. We show that this trust is fragile. In this talk, we introduce the BLE Re-Pairing Attacks (BLERP), a new class of protocol-level attacks that weaponize the standard re-pairing mechanism to overwrite trusted LTKs with attacker-controlled keys, compromising the BLE security model. We reveal six critical design flaws affecting re-pairing in the latest Bluetooth standard (v6.1), and we show how these flaws enable device impersonation and Man-in-the-Middle (MitM) attacks, even against the most secure BLE configurations. The BLERP attacks are stealthy and practical: they are "0-click" on headless devices, such as keyboards, and require a single unauthenticated interaction on smartphones. We describe the BLERP Toolkit, an open-source framework built on low-cost nRF52 hardware that enables over-the-air testing of BLE pairing and allows attendees to audit their own devices. The talk includes a live demonstration of a re-pairing Peripheral Impersonation attack against a smartphone and concludes with immediate, actionable mitigations to protect against the BLERP attacks. Attendees will learn about BLE security, how BLERP attacks undermine it, and how to defend against these newly identified threats. Tommaso Sacchetti | PhD Candidate, EURECOM Daniele Antonioli | Assistant Professor, EURECOM https://ift.tt/2lL4dVj

source https://www.youtube.com/watch?v=_08bb55Q33o

Black Hat Asia 2026 | Bad Vibes - Pwning Coding Agents 70 Times With The Same Bugs

It seems like everyone with access to a keyboard is vibe-coding these days, with an increasingly significant amount of code being written through the use of AI Coding Agents. These tools, whether specialized IDEs, IDE extensions, or CLI utilities, come with varying features, interfaces and architectures. However, they all suffer from the same fundamental, severe vulnerabilities and flawed design choices, which we will expose. Our research has uncovered 70+ serious vulnerabilities across a broad array of major platforms, including GitHub Copilot, Google Gemini CLI and Antigravity, OpenAI Codex, Claude Code, Amazon Q and Kiro, Cursor, and more. These vulnerabilities demonstrate that the industry is collectively making similar, fundamental mistakes in securing these agentic applications, which means that not only coding agents may suffer from similar problems. In this session, we will first map out what an attacker needs to achieve in order to truly compromise an AI agent. We will present a variety of issues which lead to severe consequences, making coding agents perform actions significantly more devious than just writing terrible code. Come vibe with us as we demonstrate these attacks across multiple products, showing how to turn a successful prompt injection into a devastating machine compromise using various command-line tricks, creative path manipulation techniques, and turning sandbox features against themselves to bypass established defenses. By detailing the common, critical mistakes shared across these vendors, we aim to provide developers and architects with actionable takeaways to avoid these deep-seated vulnerabilities and better secure the future of AI development tools. Philip Tsukerman | Vulnerability Research Team Lead, Cyberark Nil Ashkenazi | Cyber Security Researcher, Cyberark Alon Zahavi | Senior Security Researcher, Cyberark https://ift.tt/wUQOSoD

source https://www.youtube.com/watch?v=7UCpHzFYF40

Black Hat Asia 2026 | Large-Scale macOS PID-Domain Vulnerability Discovery with LLM Reasoning

For years, macOS researchers have focused on high-privilege system and user domain services—yet a vast class of background daemons has quietly operated beneath the radar: PID-domain services. These processes, often reachable even from sandboxed apps, expose privileged functionality and sensitive system controls. Despite their enormous attack surface, they've remained largely unexplored and unprotected—until now. In this Briefing, we will unveil the first large-scale automated framework for discovering logic vulnerabilities in PID-domain services, powered by LLM-assisted static analysis. We will start by dissecting historical flaws and Apple's patching patterns to formalize a repeatable attack model. Building on that foundation, our framework automatically enumerates connectable PID-domain daemons, decompiles their exported APIs, and leverages LLM semantic reasoning to classify sensitive operations across five categories—from file and privacy access to interprocess privilege crossing. We then map entitlements to these operations and apply taint analysis to trace attacker-controlled data into privileged sinks—surfacing hidden logic flaws that manual auditing would almost certainly miss. Our evaluation uncovered 12 previously unknown vulnerabilities, including multiple sandbox escapes and TCC privacy bypasses—six of which have already been assigned CVEs by Apple. This research exposes a massive, underestimated attack surface within macOS's userspace and demonstrates how LLMs can be weaponized for scalable vulnerability discovery in closed-source ecosystems. Attendees will gain new insights into Apple's userspace attack surface, automated bug-hunting methodologies, and the next frontier of human–AI collaboration in exploit development. l_m_h l_m_h | Independent Security Researcher Yinyi Wu | Security Researcher, Dawn Security Lab, JD.com Yingqi Shi | Security Researcher, DBAPPSecurity Yuchong Xie | Security Researcher, The Hong Kong University of Science and Technology Cheng Li | Security Researcher Yizhuo Wang | Security Researcher https://ift.tt/vSPIcka

source https://www.youtube.com/watch?v=uFgB_aMw5-g

Black Hat Asia 2026 | LLM-Empowered Differential Testing for the Ethereum Infrastructure

Securing over $380 billion in digital assets, the Ethereum ecosystem relies entirely on clients to bridge users and the blockchain network. However, this infrastructure remains perilously fragile: the infamous CVE-2020-26241, a single memory corruption bug in the dominant Geth client, triggered an unintended Ethereum mainnet chain fork, causing a catastrophic 7-hour outage for major infrastructures like Infura and MetaMask. While the community now champions "client diversity" to mitigate such single points of failure, this heterogeneity introduces a new, insidious threat: subtle implementation inconsistencies across different languages and architectures that traditional testing methods fail to detect. To fortify this multi-billion dollar foundation, we propose a novel, specification-driven differential testing framework that synergizes classical software engineering with modern AI. Unlike traditional fuzzers, our approach leverages Large Language Models (LLMs) to bridge the gap between abstract specifications and complex reality. We utilize LLMs not only to generate diverse, semantically valid test inputs (covering both EVM opcodes and Client APIs) but also to act as intelligent filters that distinguish genuine bugs from harmless semantic variations. This "dual-engine" approach allows us to identify deep logic flaws with high precision while minimizing false positives. Our comprehensive evaluation across 11 distinct clients uncovered 98 previously unknown bugs, even including critical errors within the official Ethereum specifications themselves. The impact of our work is immediate and far-reaching: developers confirmed our findings with a greater than 90% acceptance rate, 4 vulnerabilities were assigned CNVD IDs, and our methodology has received official endorsement from the Ethereum Foundation, with specific findings escalated to core protocol management meetings. We provide not just a bug-finding approach, but a crucial safeguard for the stability of the decentralized economy. Jie Ma | Eng.D Candidate, Beihang University; Zhongguancun Laboratory Ningyu He | Research Assistant Professor, The Hong Kong Polytechnic University; Amber Group Chiachih Wu | Partner & Head of Web3 Security, Amber Group Haoyu Wang | Professor, Huazhong University of Science and Technology Ying Gao | Associate Professor, Beihang University; Zhongguancun Laboratory Yinliang Yue | Professor, Zhongguancun Laboratory https://ift.tt/0v4BPuI

source https://www.youtube.com/watch?v=btaJkpmFs4s

Black Hat Asia 2026 | Overkill: Hijacking a Wi-Fi 7 Chip for SYSTEM Privileges

"A RISC-V CPU in your modern Windows PC? It is more likely than you think!" Research on Windows privilege escalation often concentrates on uncovering vulnerabilities within the kernel itself. However, this is only one part of the broader security picture. Leveraging firmware on Windows for exploits, unlike on Android, remains largely overlooked. It deserves far more attention. In this Briefing, we will take a deep dive into the driver and firmware powering MediaTek's latest client Wi-Fi 7 chip, the Filogic 360 codenamed MT7925. We will present our point of entry and describe how we leveraged both Linux and Windows drivers to get more insights into the kernel-firmware communication. Then, we will explain and showcase how we got code execution on the MT7925 device and how it enabled us to analyze the entire firmware. At the end, we will demonstrate a complete exploit chain, starting with a leak, then the takeover of the communication processor of the MT7925 from userspace, followed by the exploitation of a bug in the firmware-kernel interface to escalate privileges on the latest Windows 11 25H2 release. Nicola Stauffer | Graduate Student, Zürich University of Applied Sciences ZHAW Gürkan Gür | Senior Lecturer, Zürich University of Applied Sciences ZHAW https://ift.tt/W3MCoyY

source https://www.youtube.com/watch?v=Hr5Wla1__0k

Black Hat Asia 2026 | Mass Scale Hijacking of Shared Mobility and EV-Charging Fleets

Rentable IoT infrastructures—such as e-scooters, EV chargers, shared bicycles, laundry machines, and public tools—are now deployed at city scale and controlled through cellular IoT backends. However, our research reveals that these systems systematically rely on weak resource identifiers, inconsistent authentication models, and insecure backend APIs, enabling attackers to remotely manipulate thousands of devices without physical access. In this talk, we will present the first large-scale, cross-vendor security study of the rentable IoT ecosystem, covering 17 hardware devices and 92 mobile/mini-program apps. By reverse-engineering firmware, extracting cellular traffic from insecure modem debug channels, re-implementing binary protocols as "phantom clients," and performing black-box API testing, we uncovered 57 previously unknown vulnerabilities across 28 products. These issues enable attackers to: • Force all rentable devices offline (city-wide DoS) • Remotely lock, unlock, or disable mobility devices • Obtain free charging, free rides, or unrestricted device usage • Hijack user accounts and leak sensitive personal data • Spoof device states to mislead operators and trigger operational failures To scale these attacks, we introduce IDScope, an automated enumeration engine capable of inferring all valid device serial numbers or user IDs within minutes, bypassing rate limits and enabling exploitation of every device in a vendor's fleet. We will demonstrate end-to-end attacks on real devices—turning thousands of EV chargers into free power stations, remotely stopping active e-bikes, unlocking shared devices, and impersonating entire device fleets. The talk will conclude with practical mitigations for both vendors and cities seeking to protect smart-mobility infrastructure. Hetian Shi | Hardware and IoT Security Researcher, Tsinghua Univeristy https://ift.tt/zACt3sW

source https://www.youtube.com/watch?v=1ntsyqs5yy0

Black Hat Asia 2026 | Graph-Aware LLM for Windows Logon with a Closed-Loop Guarded Detection Agent

Because Windows Event Logs were never originally designed for detecting unauthorized logons, traces of attacks are easily buried in a massive amount of noise. It is also inherently difficult to create reliable signatures for suspicious log entries in Windows Event Logs, and research on analysis methods has been ongoing for many years. In recent years, the use of LLMs for log analysis has advanced; however, in real-world investigations, log sizes often exceed hundreds of gigabytes. In such cases, prompts quickly become too large, making it impractical to apply LLMs directly. In addition, hallucinated explanations and lack of reproducibility remain key challenges when using LLMs in security operations. In this Briefing, we will present a practical, production-ready framework that combines graph analytics with LLM agents to accurately detect suspicious logons. Concretely, we compress logs into graph information by constructing an authentication graph of users and hosts from Windows Event Logs. This makes it possible to reduce the data to a realistic size that can actually be fed to an LLM. On top of that, a closed-loop detection agent autonomously iterates the cycle of generating search queries to a database → executing the queries → evaluating the results → exploring further. Through this loop, it detects signs such as concentrations of service tickets, cross-host logons by privileged accounts, remote service access, and suspicious chains of logons. The outcome of the analysis is presented as an incident severity level, an evidence timeline, and an attack scenario summary, automatically providing information that can be directly used in real incident investigations. Our approach aggregates millions of events down to a few dozen suspicious logons within minutes, and elevates LLM usage in DFIR into a form that is auditable, reproducible, and operationally viable. We will release an open-source tool that implements this method so that analysts can apply it to real-world incident analysis. Shusei Tomonaga | CTO, JPCERT/CC https://ift.tt/B2jZ1tL

source https://www.youtube.com/watch?v=7QVcpQA_j_I

Black Hat Asia 2026 | Social Media Manipulation Wargaming for Cyberliteracy and Research

LLM-powered bots are influencing social media around the world, amplifying the effects of misinformation and disinformation. Detecting if content is being promoted or manipulated by such bots is difficult, both for casual observers of social media platforms as well as for platform operators faced with continuous "cat and mouse" development by malicious adversaries. To address both challenges, we produced "Capture the Narrative", the world's first CTF-style massively-multiplayer wargame for social media manipulation. Over four weeks in 2025, 288 students formed 70+ teams from Australian universities and competed in a multiplayer competition to manipulate an in-house social media platform we called "Legit Social". They were aiming to swing a simulated election in a fictional country, a task which they ultimately completed by moving the electorate by 1.8 percentage points. In this Briefing, I will introduce the competition, as well as discuss its outputs, which include the associated awareness campaign as well as a collaboratively developed dataset made up of survey data, developed bot software, and recorded bot activity for the thousands of bots producing millions of artificial posts to amplify in-game misinformation. Hammond Pearce | Senior Lecturer, UNSW (University of New South Wales) https://ift.tt/A7mkBlS

source https://www.youtube.com/watch?v=zihOGGeoaTU

Black Hat Asia 2026 | When 3.5 Billion Strangers Can Exploit Your WhatsApp Devices

WhatsApp evolved from a simple text-messaging app serving thousands of users in 2009 to the world's largest messaging platform with 3.5 billion users and 30+ complex message types, but its original "anyone can contact anyone" architecture remained unchanged. This creates a systemic security risk where users' attack surface grows linearly with both features and users. In this talk, we will demonstrate how attackers abuse WhatsApp's unrestricted access architecture for attacks ranging from mundane spam to APT zero-click remote code execution exploits. We will present two novel attack classes responsibly disclosed to Meta: Class I attacks that exploit nonconsensual message delivery, and Class II attacks that violate user privacy by extracting metadata directly from WhatsApp servers. We'll reveal our new device fingerprinting techniques leveraging implementation side-channels in E2EE and messages, release an open source WhatsApp research tool demonstrating these attacks, and provide live exploitation demos against test accounts. Moving to defenses, we will reveal why current mitigations fail because they treat individual symptoms rather than the architectural root cause, creating a perpetual whack-a-mole cycle where each new feature potentially introduces new vulnerability classes. To thoroughly solve this issue, we will present a viable architectural solution that dramatically reduces the attack surface while maintaining strong privacy guarantees. Most critically, this solution makes the attack surface fixed with respect to WhatsApp's expansion in users and features, transforming security from reactive patching to secure by default. Tal Be'ery | CTO, Co-Founder, Zengo (acquired by eToro) https://ift.tt/KIcYoCM

source https://www.youtube.com/watch?v=FXV0LWPcB64

Black Hat Asia 2026 | Shedding LIGHT on Real-World Attacks on Cloudless IoT Devices

The rapidly expanding Internet of Things (IoT) landscape is shifting toward cloudless architectures, removing reliance on centralized cloud services but exposing devices directly to the internet and increasing their vulnerability to cyberattacks. Our research revealed an unexpected pattern of substantial Tor network traffic targeting cloudless IoT devices, suggesting that attackers are using Tor to anonymously exploit undisclosed vulnerabilities (possibly obtained from underground markets). To delve deeper into this phenomenon, we developed TORCHLIGHT, a tool designed to detect both known and unknown threats targeting cloudless IoT devices by analyzing Tor traffic. TORCHLIGHT filters traffic via specific IP patterns, strategically deploys virtual private server (VPS) nodes for cost-effective detection, and uses a chain-of-thought (CoT) process with large language models (LLMs) for accurate threat identification. Our results are significant: for the first time, we have demonstrated that attackers are indeed using Tor to conceal their identities while targeting cloudless IoT devices. Over a period of 12 months, TORCHLIGHT analyzed 26 TB of traffic, revealing 45 vulnerabilities, including 29 zero-day exploits with 25 CVE-IDs assigned (5 CRITICAL, 3 HIGH, 16 MEDIUM, and 1 LOW) and an estimated value of approximately $312,000. These vulnerabilities affect around 12.71 million devices across 148 countries, exposing them to severe risks such as information disclosure, authentication bypass, and arbitrary command execution. The findings have attracted significant attention, sparking widespread discussion in cybersecurity circles, reaching the top 25 on Hacker News, and generating over 190,000 views. Yumingzhi Pan | Ph.D. Student, Southeast University Zhen Ling | Professor, Southeast University Yue Zhang | Professor, Shandong University Hongze Wang | Ph.D. Student, Southeast University Guangchi Liu | Professor, Southeast University Junzhou Luo | Professor, Southeast University https://ift.tt/QDxGp3A

source https://www.youtube.com/watch?v=UM-ej4hO9_U

Black Hat Asia 2026 | Exploiting DFIR Agents Through Adversarial Manipulation

In recent years, Digital Forensics and Incident Response (DFIR) tools have increasingly adopted Large Language Models (LLMs) to enhance automation, analysis, and reporting. Prominent examples include Velociraptor's MCP integration and Timesketch's AI Summary feature. This study empirically demonstrates that attackers can exploit prompt injection through boundary perturbation of structured data—a form previously considered resistant to manipulation. Importantly, this issue is not specific to any single tool; rather, it represents a broader class of risks that emerges whenever DFIR tools are integrated into autonomous LLM agents. By embedding malicious instructions into routine forensic artifacts such as logs and scheduled tasks, adversaries can cause DFIR LLM agents to misinterpret benign data as instructions, leading to three outcomes: Hide, Mislead, and Exploit. To the best of my knowledge, this is the first work to demonstrate structured-data injection attacks in LLM-integrated DFIR environments. The study also proposes practical defense-in-depth countermeasures, including enforcing least privilege, mandating strict structured output validation, and maintaining human-in-the-loop verification to ensure the reliability and safety of automated DFIR workflows. This Briefing aims to provide organizations advancing DFIR automation with LLM agents a foundation for rethinking, at the design level, how much autonomy should be granted to such agents and where human oversight must remain integral. Yusuke Nakajima | Security Analyst, NTTDATA https://ift.tt/RftH9kP

source https://www.youtube.com/watch?v=sK8omfWUMaM