Scope is an autonomous penetration-testing agent that mimics an ethical hacker while staying inside a pre‑approved range (GitHub). Built for the Indie Dev track of the Meta Global AI Developer Hackathon, it aims to fix common gaps in AI security demos.
The system runs on Meta's muse-spark-1.3 model accessed through the Meta Model API. The model drives the reasoning loop, chooses the next probe, reads error messages to tweak payloads, and decides if an exploit succeeded.
Project documentation states that a finding counts only when the exploit actually works. The agent therefore depends on reproducible requests and responses that demonstrate the vulnerability, not on speculation or potential issues.
The workflow repeats a cycle of reconnaissance, hypothesis generation, verification, and recording. The agent scans the target, names a specific testable weakness, validates it with real HTTP requests, and stores the evidence together with remediation steps.
Safety is enforced by a Scope Guard that applies a deny‑by‑default allowlist in code before each action. Anything outside the authorized scope is refused and logged, leaving the model unable to bypass the restriction.
Every tool call passes through the Scope Guard, while each model call goes through a Cost Guard that tracks token usage and financial cost with a hard stop. The agent uses function and tool calls for actions such as HTTP requests, Python scripts, and port scans.
The project produces Markdown reports that include findings and full trace transcripts. The loop runs until the agent uncovers solid findings or exhausts its allocated steps.
The design of Scope suggests an emerging architectural pattern for autonomous security agents, where hardcoded guardrails restrict execution paths rather than relying on model prompt compliance. By enforcing deny-by-default checks at the code level prior to tool execution, the framework attempts to mitigate the risk of unintended target expansion during automated reconnaissance and exploitation attempts.
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