Proposed Contributions & Extensions

[Insider77Circle]

Hey,
First — the benchmark methodology was the right call. Testing against a cleaned, hint-free XBOW and publishing the four failures openly is exactly the kind of intellectual honesty that makes a tool worth building on. That's why I'm reaching out.
I'm building a series of extensions on top of Shannon and wanted to give you visibility before anything goes public. These are designed to be additive, not competitive.

1. Adversarial Mutation Engine + Intelligent Router (Codename: PIVOT)
   Shannon's four documented failures share a common root — when an agent hits a wall, it handles the dead-end locally with no external escalation path. PIVOT is a central mutation engine that all agents and operational phases yield to when a pivot is needed, rather than attempting to resolve obstacles in isolation.
   The core is an intelligent router that classifies each obstacle and decides whether it belongs in a deterministic processing lane or an LLM freestyle lane. Hard, pattern-matchable obstacles (WAF blocks, character filters, known error signatures) route deterministically. Ambiguous, novel, or composite obstacles route to a constrained LLM pass. The router learns between engagements by using terminal output as feedback — misrouted obstacles shift routing weights over time, and the deterministic signature library grows empirically from real engagements rather than staying theoretically static.
   This directly addresses the JSFuck payload failure (EncodingMutator with full obfuscation family library), the SSTI misclassification (forced explicit escalation — no silent abandonment), and the general payload knowledge gap (mutation families that compound across engagements).

2. OSINT Recon Agent — Black Box / Grey Box Extension
   Shannon currently requires source code access. This locks it out of a significant portion of real-world engagements where source isn't available. I'm building a recon-first agent that runs before the code analysis phase and constructs a pseudo-whitebox model through crawling, header fingerprinting, API schema inference, error analysis, and response pattern mapping.
   This extends Shannon's utility to black box and grey box scenarios without changing the core architecture — the recon agent's output feeds into Shannon's existing analysis pipeline as a synthetic source model. Same downstream, wider intake.

3. Anti-Hallucination Checkpoint Layer
   The deterministic scoring engine acts as a hard checkpoint between LLM suggestions and execution. The model never evaluates exploit outcomes — it only handles obstacle classification and strategy suggestion. All scoring, signal detection, and escalation decisions are deterministic rules measured against observable response deltas (status codes, body length, timing distributions, error class changes, payload reflection).
   Every freestyle LLM suggestion is validated against a strict JSON schema before it touches the execution layer. Failed validations trigger one retry, then structured abandonment with a full trace — never silent failure. This is designed to push accuracy meaningfully beyond the current benchmark number with a fully auditable trace for every engagement.

Happy to share more detail on any of these, run a private benchmark comparison before anything goes public, or explore whether any of this belongs upstream rather than as a fork. Open to collaboration if that makes sense on your end.