Alf FAQs

Question 1

What are Alf's core capabilities for crash analysis?

Accepted Answer

Alf provides a comprehensive crash analysis pipeline including triage (initial assessment), classify (categorization), report (detailed reporting), minimize (reducing crash inputs), and targeted corpus generation to refine fuzzing efforts, all driven by AI.

Question 2

What is Alf and what is its primary purpose?

Accepted Answer

Alf is an agentic AI-driven tool designed to orchestrate LLDB for automating crash exploration, injecting mutation hooks, and generating targeted fuzzing corpus. It is specifically focused on Apple Mach-O targets on arm64(e) architectures.

Question 3

What is the Model Context Protocol (MCP) server in Alf?

Accepted Answer

The interactive MCP server in Alf exposes over 40 LLDB tools, allowing large language models like Claude, Gemini, or GPT to drive the debugger interactively. This facilitates deeper exploration and analysis beyond automated pipelines.

Question 4

What types of fuzzing engines does Alf provide?

Accepted Answer

Alf offers three powerful fuzzing engines: an LLM-driven fuzzer with mutation hooks, a hybrid fuzzer combining LLM cold-start with native libFuzzer, and integration with Jackalope/TinyInst for advanced macOS framework fuzzing.

Question 5

How does Alf leverage AI in its functionality?

Accepted Answer

Alf uses AI via the Model Context Protocol (MCP) to drive LLDB. This enables AI for automated post-mortem crash analysis pipelines (triage, classify, report, minimize), intelligent corpus generation, and an end-to-end AI director mode for continuous analysis loops.

Alf

Alf

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