ReaperAI: DAW & Cybersecurity Automation

• ReaperAI is a set of AI-integrated systems designed for both digital audio workstation automation and offensive cybersecurity, emphasizing domain-specific language model integration.
• In music production, systems like DAWZY and Composer’s Assistant 2 use human-in-the-loop workflows, atomic Lua script execution, and advanced MIDI infilling techniques for precise control.
• The cybersecurity module employs modular agents following the MITRE ATT&CK framework, orchestrating reconnaissance, vulnerability analysis, exploitation, and post-exploitation tasks with robust safety checks.

ReaperAI is a designation used for a set of independent AI-integrated systems in two prominent domains: digital audio workstation (DAW) automation and offensive cybersecurity. These systems are characterized by their domain-specific LLM integration, fine-grained tool augmentation, and close coupling with host software (REAPER DAW and penetration testing environments, respectively). The following sections detail the technical foundations, architectures, operational principles, and evaluation outcomes of ReaperAI systems as documented in the literature.

1. System Architectures

1.1 DAW Automation: DAWZY and Composer's Assistant 2

ReaperAI-based systems in music production, such as DAWZY and Composer's Assistant 2, implement tightly integrated human-in-the-loop workflows inside the REAPER DAW. DAWZY consists of three primary layers: User Interaction (Electron.js host with chat and minimal controls), Processing (Whisper for speech recognition, BasicPitch for hum-to-MIDI, GPT-5 for language-to-script translation, and Model Context Protocol (MCP) tools), and Execution (state serialization, atomic script execution via ReaPy, and utility scripts). Composer's Assistant 2 is centered around a T5-style encoder–decoder transformer that performs multi-track MIDI infilling with fine-grained user controls, with interaction mediated by Python backends and dockable GUIs in REAPER (Elkins et al., 2 Dec 2025, Malandro, 2024).

1.2 Offensive Security: Autonomous Pentesting Agent

In cybersecurity, ReaperAI refers to a modular, fully autonomous AI penetration testing agent. Its architecture comprises four functional modules: Reconnaissance Agent, Vulnerability Analysis Agent, Exploitation Agent, and Post-Exploitation Agent. These agents communicate with a central orchestrator implemented as a Python wrapper, responsible for maintaining state, enforcing constraints, and handling retrieval-augmented generation (RAG) tasks with GPT-4 as the LLM backend. Context and memory are managed with PostgreSQL-based vector search to supply relevant historical data within LLM prompts (Valencia, 2024).

2. Methodological Foundations

2.1 LLM-Orchestrated Code and Command Generation

Across both DAW and cybersecurity domains, ReaperAI relies on prompt engineering pipelines to ground high-level natural language intent into executable actions. In DAWZY, user requests are translated to atomic Lua scripts via GPT-5, constrained by a prompt containing serialized DAW state and tool invocation specifications (i.e., state_query, fxparam, beat_gen). The LLM emits scripts bracketed by undo markers to guarantee reversibility and safety (Elkins et al., 2 Dec 2025). In pentesting, prompting pipelines implement role prompting, chain-of-prompting, and chain-of-thought reasoning to decompose and sequence tasks—initiating system commands, exploits, and post-analysis automatically using subprocess launches and context optimization (Valencia, 2024).

2.2 Tooling Protocols and Controls

DAW Model Context Protocol (MCP) Tools

• state_query(): Returns current tracks, items, FX, and parameter ranges in JSON format, enabling precise LLM grounding.
• fxparam(): Maps human-scale audio parameter requests (e.g., "+3dB") to ReaScript's normalized ranges.
• beat_gen(): Runs local AudioGen/MusicGen models, returning generative audio artifacts for session import.

Restricting LLM actions to these tools eliminates hallucinated API calls and enforces state consistency (Elkins et al., 2 Dec 2025).

Advanced MIDI Infilling Controls

Composer's Assistant 2 supports fine-grained controls (horizontal/vertical density, pitch-class, rhythmic interest, step/leap propensity, DNOC, pitch range, 1D/2D rhythmic conditioning) encoded as quantized tokens concatenated to encoder and decoder input sequences, allowing real-time steering of output statistics and structure (Malandro, 2024).

Pentesting Task-Orchestration and RAG

The offensive security agent sequences tasks according to the MITRE ATT&CK framework, generating, executing, and revising shell commands using LLM-guided templates and adjusting strategy based on memory retrieval from task tree states, command histories, and analysis logs (Valencia, 2024).

3. Safety, Reversibility, and Ethical Constraints

3.1 DAW Workflow Guarantees

Every DAWZY action is atomic and fully undoable, achieved by mandating Lua scripts to start and end with Rea.Undo_BeginBlock() and Rea.Undo_EndBlock(). No script batches unrelated actions, and interface-level undo calls revert the session to the exact previous state. This ensures robust error recovery and user trust during iterative creative processes (Elkins et al., 2 Dec 2025).

3.2 Security Agent Operational and Ethical Controls

ReaperAI's cybersecurity deployments embed operational checks such as prompt-level target scope constraints, comprehensive logging, audit trails, and access control for isolated environments. The agent is designed for lab-only use, with provisions for human-in-the-loop overrides and time-boxed/whitelisted engagements. Error handling for interactive shell commands remains a challenge, partially addressed via pexpect, but exceptions and unauthorized scanning are still failure modes (Valencia, 2024).

4. User Interaction and Interface Design

4.1 DAW Systems

ReaperAI's DAW interfaces are intentionally minimal. DAWZY features a single chat box with voice/text/hum input, basic transport buttons, and "Record Hum" for melody sketches. System responses always include plain English explanations, supporting instant feedback and user education. Composer's Assistant 2 provides a dockable control panel for adjusting per-track/measure controls, further facilitating interactive co-creation (Elkins et al., 2 Dec 2025, Malandro, 2024).

4.2 Security Agent

The pentesting ReaperAI operates primarily via command-line interface and Python scripts, with all LLM-initiated commands and decisions articulated via prompts. User engagement is typically limited to initial configuration and optional human intervention in ambiguous or high-risk situations (Valencia, 2024).

5. Evaluation Procedures and Outcomes

5.1 DAW Automation Results

Objective task reliability (multi-instruction FX, GUI navigation, automation, education) was validated with 100% success by GPT-5/DAWZY on music production tasks and 25–50% for open-source models, which failed primarily on script validation and state tracking. Subjective mean opinion scores (MOS) from 21 users placed DAWZY above neutral across Enjoyment (4.48/5), Learning, Collaboration, Usability, and Control. Comparative creative tasks against an Ableton-MCP system showed DAWZY succeeded in 78% of trials versus 0% for the baseline (Elkins et al., 2 Dec 2025).

Composer's Assistant 2 exhibited statistically significant improvements in objective metrics (F1 score for infilling, entropy, groove similarity) over previous systems. In listening studies with 28 participants, co-created infillings were statistically indistinguishable from real, non-AI tracks across all axes measured (Malandro, 2024).

5.2 Offensive Security Results

In controlled Hack The Box experiments, ReaperAI achieved a 100% exploitation rate on “Blue” (EternalBlue), with mean time-to-exploit of 95s (σ=12s), and 60% on “Lame” with mean 150s (σ=25s). The system demonstrated high consistency and >80% coverage of recon-to-exploit chains but struggled with interactive scenarios and scope drift (Valencia, 2024).

6. Future Directions

Planned research and engineering directions include:

• DAW Automation: Increased robustness of tool invocation, more sophisticated musical context modeling, and extension to broader creative modalities (Elkins et al., 2 Dec 2025, Malandro, 2024).
• Security Agent: Enhanced command-line interaction via specialized LLM tuning, integration of longer context windows, standardized output schemas, cybersecurity-specific embedding models, operator-in-the-loop reinforcement learning, and community benchmarks for quantitative assessment (Valencia, 2024).

7. Scope, Limitations, and Impact

ReaperAI systems exemplify the integration of LLMs and tool protocols for both creative and security-critical automation. In both domains, the emphasis is on maintaining groundedness, reversibility, and user oversight—whether via atomic script generation and articulate explanation in DAWs, or scope-constrained, auditable automation in offensive cybersecurity. Current limitations primarily concern interactive error handling and the challenges of mapping high-level requests to valid, safe, low-level actions, especially as domains grow in complexity and real-world variability. Ongoing refinements target these gaps directly, with substantial potential impact on workflows in both music technology and cybersecurity practice (Elkins et al., 2 Dec 2025, Valencia, 2024, Malandro, 2024).