Black Hat Stories | Founder and Creator of Black Hat

Cybersecurity is constantly evolving. 🔐 Hear from Jeff Moss on the realities shaping the industry today.

source https://www.youtube.com/shorts/MP757qc0rbw

Founder and Creator of Black Hat | Jeff Moss

Black Hat Founder Jeff Moss talks about Black Hat, the evolution of cybersecurity, and what it takes to stay one step ahead.

source https://www.youtube.com/shorts/E9rG0QNF7uQ

Black Hat Stories | Jeff Moss

Black Hat brings together the people, perspectives, and expertise that help cybersecurity professionals stay one step ahead.

source https://www.youtube.com/shorts/4mmHnhaMf-4

Black Hat Asia 2026 | Mobile Track Spotlight

Mobile security continues to evolve at a breakneck pace, with new attack surfaces, ecosystem shifts, and research breakthroughs reshaping the landscape every year. In this interactive session, members of the Black Hat Asia Review Board will highlight the trends, techniques, and vulnerabilities that stood out during this year's Briefings review cycle. Join us for a fast paced discussion on what's emerging, what's escalating, and what practitioners should be paying attention to next. Bring your questions—this session is designed to be conversational and candid. Anant Shrivastava | Founder, Cyfinoid Research Pamela O'Shea | Director, Shea Security Shanna Daly | CEO, Torin Cyber Group https://ift.tt/vSWGwVg

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

Black Hat Stories | Jeff Moss, Founder and Creator of Black Hat

In this episode, Black Hat Founder Jeff Moss reflects on the evolution of cybersecurity, the rise of AI and automation, and the growing challenge of separating trustworthy insights from an overwhelming volume of information. In a world of endless content, Black Hat continues to bring together the people, perspectives, and expertise that help cybersecurity professionals learn, connect, and navigate an increasingly complex landscape.

source https://www.youtube.com/watch?v=HbPqeqUm9fQ

Black Hat Asia 2026 | Revealing User Activity on macOS for Apple Silicon

Apple's M-series now powers a huge portion of executive, enterprise, and developer laptops. One blind spot has stayed largely off the radar: interrupt-based side channels where signals raised by normal device activities such as networking, input, and display can be sensed by an unprivileged attacker. We show that a determined attacker can turn those signals into high-fidelity surveillance of user activities on macOS for Apple Silicon. In this talk, we will present TIDE, which works like a stethoscope for the OS: every time macOS returns from the kernel to user space, it produces a tiny, deterministic "heartbeat we can feel from user space". By listening for that heartbeat, TIDE pinpoints exactly when an interrupt occurs without any timers. With TIDE as our sensor, we reverse-engineer Apple's publicly undocumented interrupt delivery and reveal that shared peripheral interrupts are uniformly distributed across all active cores. This quirk means an unprivileged attacker no longer has to "chase the right core" to spy on user activities within the same OS. To demonstrate the effectiveness of TIDE, we will present two live, end-to-end attacks on real Apple Silicon hardware. One is website fingerprinting on Safari with about 94% Top-1 accuracy in closed-world and about 91% in open-world to reveal the websites users have visited. The other is Video fingerprinting with roughly 80% to identifying streaming content from its interrupt patterns. We conclude with potential software-only mitigations Apple can deploy, plus longer-term OS/SoC directions. We call for more parties to join in this effort to enhance the security of macOS. Xin Zhang | Ph.D. Student, Peking University Zhi Zhang | Senior Lecturer, The University of Western Australia Chang Liu | Ph.D. Student, Tsinghua University Qingni Shen | Full Professor, Peking University Trevor E. Carlson | Associate Professor, National University of Singapore https://ift.tt/tNBT93X

source https://www.youtube.com/watch?v=PuQIMyz0BeQ

Black Hat Asia 2026 | Model Files → Memory Corruption → RCE: The Triple-Stage AI Attack Chain

Amidst the rapid advancement of artificial intelligence technologies, an increasing number of enterprises and individuals are adopting AI solutions. As the core vessel of AI systems, model files encapsulate substantial training outcomes and intellectual achievements from researchers. With the proliferation of large language models and the maturation of open-source communities, leading organizations are actively promoting model open-sourcing and sharing, making cutting-edge models accessible to developers worldwide. However, in practical applications, the model loading process has emerged as a critical security vulnerability hotspot. Existing research reveals significant security risks in the model loading mechanisms of mainstream deep learning frameworks. For instance, PyTorch's historical use of pickle for model serialization introduces inherent deserialization vulnerabilities, while TensorFlow is susceptible to remote code execution (RCE) through maliciously crafted Lambda Layers. More alarmingly, as these frameworks predominantly employ C/C++ implementations for high-performance computing, they remain exposed to conventional memory safety threats such as buffer overflows. This raises a crucial question: Can these memory vulnerabilities be weaponized into complete and reliable RCE attack chains? In this Briefing, to the best of our knowledge, we will present the first publicly disclosed study that systematically exploits memory corruption vulnerabilities in AI model files to achieve reliable remote code execution. By analyzing the memory management mechanisms of mainstream deep learning frameworks, we construct a complete, end-to-end three-stage attack chain — from malicious model files to arbitrary code execution — through carefully designed heap layouts and control-flow hijacking techniques. We further validate the practical exploitability of this attack chain across real-world AI inference systems. Ji'an Zhou | Security Researcher Lei Lu | Security Researcher Li'shuo Song | Security Researcher https://ift.tt/o07khlr

source https://www.youtube.com/watch?v=nQml4Ng9iVc

Black Hat Asia 2026 | Payload Compromised: Full Key Recovery in Rocket.Chat E2EE

Rocket.Chat is used in more than 150 countries, where many organizations rely on its end-to-end encryption (E2EE) for security-critical communication. This talk presents the first comprehensive analysis of Rocket.Chat's E2EE as deployed in real systems. By combining automated symbolic analysis with in-depth manual inspection of the implementation, we identify practical attacks that break both confidentiality and integrity. Our most severe finding is a practical key-recovery attack. An attacker with access to encrypted user backups can recover private keys and all derived group keys in twelve days under realistic assumptions. We validate this attack with practical proof-of-concept exploits. This results from weak offline-attack resistance combined with a biased, low-entropy password generator used to encrypt key backups. At the time of reporting, any server operating under a malicious-server threat model could execute the attack. We also uncover structural failures in the platform's key-rotation workflow. Although E2EE passwords and a master key were rotated, the group keys protecting message content were never replaced. Clients continued to accept and redistribute compromised group keys, which were then reused to encrypt new messages and decrypt past ones. A single key recovery therefore enabled expanding and persistent compromise across group communication. Manual analysis further revealed integrity failures, including ciphertext forgery made possible by unauthenticated AES-CBC encryption. Beyond the technical flaws, we also reconstruct how this fragile design emerged by examining public development discussions and historical commits. This OSINT analysis explains why several fundamental E2EE principles were never integrated and how long-term structural risks accumulated. The most severe vulnerabilities, including key recovery and broken key rotation, were fixed within six months of disclosure, and remaining integrity issues were patched after extended coordination. Attendees will learn how to analyze real-world E2EE systems, detect specification-implementation gaps, and replace password-based architectures with modern best practices. Hayato Kimura | Researcher, National Institute of Information and Communications Technology & The University of Osaka Ryoma Ito | Senior Researcher, National Institute of Information and Communications Technology Kazuhiko Minematsu | Research Fellow, NEC Corporation Takanori Isobe | Professor, The University of Osaka https://ift.tt/5f7G03M

source https://www.youtube.com/watch?v=35kun8wzFRU

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