Our Research
From fundamental vulnerability discovery to applied cryptography and machine learning for threat detection — our work spans the full spectrum of modern cybersecurity.
What We Work On
iOS & Mobile Security
In-depth analysis of iOS internals, Secure Enclave, kernel exploit mitigation bypasses, and mobile application security. We have presented work at USENIX and BlackHat.
IoT & Firmware Security
Binary analysis, fuzzing, and symbolic execution of embedded firmware from consumer routers, smart home devices, and industrial controllers.
Vulnerability Research & CVE Disclosure
Proactive identification of vulnerabilities, coordinated disclosure, and publication of technical advisories. We follow responsible disclosure best practices.
AI for Security
Leveraging large language models and machine learning for malware classification, automated vulnerability detection, and threat intelligence correlation.
Papers
Biting the Apple: Novel Attack Vectors in iOS Secure Enclave
[Author A], [Author B], [Author C] — USENIX Security Symposium 2025, Philadelphia, PA.
FirmBite: Scalable Firmware Fuzzing for Consumer IoT Devices
[Author A], [Author D] — IEEE Symposium on Security and Privacy 2024, San Francisco, CA.
LLMalysis: Zero-Shot Malware Classification with Large Language Models
[Author B], [Author E], [Author F] — ACM CCS 2023, Copenhagen, Denmark.
Responsible Disclosures
Zero-Day in Popular IoT Framework
Remote code execution vulnerability in the MQTT broker implementation. Coordinated disclosure with vendor, patch released within 45 days.
Authentication Bypass in Smart Home Hub
Improper session handling allowing unauthenticated API access. Full disclosure coordinated with manufacturer.
Tools & Frameworks
LLMalysis
LLM-powered malware analysis and classification pipeline.
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