Offensive Security Researcher — Browser Vulnerability Research & Agentic AI

Description

We are seeking an Offensive Security Researcher with a focus on modern web browsers and a strong interest in applying agentic AI workflows to vulnerability research.

The role involves identifying, analyzing, and exploiting vulnerabilities in browser engines and related components, while helping design and integrate AI-assisted workflows that make advanced security research more effective. This includes using AI agents, LLM-based tooling, and automated analysis pipelines to support code exploration, patch diffing, crash triage, root-cause analysis, fuzzing workflows, variant discovery, and exploit development.

This is not a prompt engineering role. We are looking for someone with real vulnerability research depth who wants to explore how agentic AI can augment expert researchers working on complex, high-value targets.

Responsibilities

• Discover and analyze vulnerabilities in JavaScript engines, rendering pipelines, IPC mechanisms, browser sandboxes, and related components.

• Reverse engineer browser internals, including JIT compilers, garbage collectors, memory management subsystems, object models, and browser security boundaries.

• Develop proof-of-concept exploits and produce clear, rigorous technical documentation.

• Track modern browser security mitigations and assess their effectiveness against real-world exploitation techniques.

• Use fuzzing, crash analysis, patch diffing, source-code auditing, and variant analysis to identify high-value vulnerability classes.

• Contribute to the design and integration of agentic AI workflows for vulnerability research, including AI-assisted code review, crash triage, root-cause analysis, patch analysis, and tool orchestration.

• Work with AI/LLM-enabled systems that interact with security tools, code search platforms, debuggers, fuzzers, static analysis frameworks, and reverse engineering environments.

• Evaluate where AI agents can meaningfully accelerate vulnerability research, and where human expertise, validation, and skepticism remain essential.

• Collaborate with vulnerability researchers, exploit developers, reverse engineers, and AI-focused engineers on high-impact offensive security projects.

Requirements

• Strong expertise in C/C++ and low-level programming.

• Experience in reverse engineering, fuzzing, vulnerability discovery, and exploit development.

• Knowledge of modern browser architectures and JavaScript engines such as V8, SpiderMonkey, JavaScriptCore, Blink, WebKit, Gecko, or Chromium internals.

• Familiarity with browser security models, sandboxing technologies, IPC mechanisms, memory corruption primitives, and modern exploitation mitigations.

• Ability to analyze large and complex codebases independently.

• Experience with debugging and reverse engineering tools such as LLDB, GDB, WinDbg, rr, IDA Pro, Ghidra, Binary Ninja, sanitizers, or browser-specific debugging infrastructure.

• Strong scripting and automation skills, preferably in Python, JavaScript, TypeScript, or similar languages.

• Interest in AI-assisted vulnerability research, agentic workflows, LLM tool use, automated code analysis, or AI-assisted fuzzing and triage.

• Ability to critically evaluate AI-generated output and separate useful research signals from hallucinations, noise, and false positives.

• Proven track record in vulnerability research, CVEs, exploit development, bug bounty research, Pwn2Own-style research, or equivalent private research is highly valued.

Nice to Have

• Experience discovering or exploiting vulnerabilities in real browser targets.

• Deep knowledge of one or more browser subsystems, such as JIT compilers, garbage collectors, DOM, WASM, graphics, media, networking, IPC, or sandboxing.

• Experience with coverage-guided fuzzing, custom harness development, corpus management, crash deduplication, or large-scale fuzzing infrastructure.

• Experience with patch diffing, variant analysis, exploit reliability, or mitigation bypass research.

• Experience integrating LLMs or AI agents with tools such as CodeQL, Semgrep, Ghidra, IDA, debuggers, fuzzers, source-code indexes, or custom static/dynamic analysis systems.

• Experience working with local or restricted AI deployments, open-weight models, RAG systems, or secure environments where sensitive code and research artifacts cannot be sent to external services.

• Interest in building internal tooling that makes expert vulnerability researchers faster, more systematic, and more effective.