Reverse engineering a Viking VOIP phone protocol with Claude Code — Boris Starkov, Eleven Labs

Claude code can be used not just to build applications but to reverse engineer legacy hardware by systematically discovering communication protocols, enablin...

By Sean Weldon

Reverse Engineering Legacy Hardware Protocols Using AI-Assisted Code Generation: A Case Study of Viking VoIP Phone Integration

Abstract

This paper examines the application of AI-assisted code generation tools, specifically Claude Code, for reverse engineering undocumented communication protocols in legacy hardware systems. Through a case study involving a Viking VoIP phone with Windows XP-era proprietary software, the research demonstrates how systematic protocol discovery can enable modern integration of obsolete hardware. The methodology employed port scanning, command enumeration, TCP proxy interception, and checksum reverse engineering to fully decode the phone's two-layer protocol structure. Key findings include the discovery of 80 valid two-letter command codes from 676 possible combinations and the identification of a simple single-byte subtraction checksum mechanism. The research resulted in direct phone programming capabilities without proprietary software dependencies and successful integration with modern conversational AI systems. This work illustrates a replicable framework for legacy hardware modernization and demonstrates a novel human-AI collaboration model where AI provides algorithmic orchestration while humans serve as physical execution agents.

1. Introduction

The rapid obsolescence of hardware platforms presents significant challenges for organizations seeking to repurpose or integrate legacy devices into modern infrastructure. When proprietary software becomes incompatible with current operating systems and manufacturer documentation is unavailable, these devices often become electronic waste despite functional hardware capabilities. This challenge is particularly acute for specialized equipment such as enterprise VoIP phones, where communication protocols were designed for closed ecosystems and never publicly documented.

Reverse engineering—the process of discovering the technological principles of a device through analysis of its structure and function—traditionally requires specialized security expertise and significant time investment. The emergence of AI-assisted code generation tools presents new methodological possibilities for protocol discovery and hardware integration. These tools can systematically explore protocol spaces, identify patterns in communication sequences, and automate the iterative testing required to decode proprietary systems.

This paper presents a case study examining how Claude Code, an AI code generation system, was employed to reverse engineer a Viking VoIP phone's undocumented communication protocol. The central research question addresses whether AI-assisted tools can enable non-security-specialists to successfully decode legacy hardware protocols and achieve modern system integration. The analysis covers the complete reverse engineering process, from initial hardware setup challenges through final integration with contemporary conversational AI infrastructure, documenting technical discoveries and the human-AI collaboration model that emerged.

2. Background and Related Work

Protocol reverse engineering has historically relied on man-in-the-middle (MITM) attack patterns, where traffic between legitimate communication endpoints is intercepted and analyzed to infer protocol structure. This approach requires establishing a TCP proxy—middleware that sits between client and server, logging all communication while transparently forwarding packets. Traditional implementations demand deep networking knowledge and manual pattern recognition across captured traffic.

The brute force enumeration methodology involves systematically testing all possible values within a defined space to identify valid inputs. When applied to command discovery, this technique sends each potential command to the target system and classifies responses as valid or invalid based on error messages or behavioral differences. While computationally straightforward, brute force approaches generate large datasets requiring careful analysis to extract meaningful patterns.

Legacy hardware integration challenges are compounded when devices require deprecated operating systems for configuration. Virtual machine (VM) environments can provide compatibility layers, but network bridging limitations—particularly with Wi-Fi adapters on macOS systems—often prevent VMs from accessing local network devices. These infrastructure constraints necessitate creative solutions for establishing communication channels between modern computers and obsolete hardware protocols.

3. Core Analysis

3.1 Initial Hardware Discovery and Infrastructure Constraints

The Viking VoIP phone under investigation connected via Power over Ethernet (PoE), providing both electrical power and network connectivity through a single cable. Initial attempts to configure the device using its proprietary Windows XP-compatible software failed due to hardware incompatibility—the research team possessed only Mac laptops, while the phone's drivers were unavailable for macOS. Previous attempts by three senior engineers using ChatGPT over a one-year period had proven unsuccessful in establishing functional communication.

The infrastructure solution involved deploying a Windows virtual machine using UTM to run the proprietary software. However, this introduced a critical network topology problem: the VM could not bridge the laptop's Wi-Fi connection to access the router where the phone was connected. This limitation prevented use of the manufacturer's configuration tools and necessitated direct protocol-level communication. The constraint ultimately proved beneficial, as it forced complete protocol reverse engineering rather than reliance on proprietary software dependencies.

3.2 Port Discovery and Command Structure Identification

Protocol discovery began with nmap port scanning to identify active communication endpoints on the phone. Initial testing of port 1001—designated as an "electronics tunnel"—proved incorrect, requiring iteration to locate the actual command interface port. Once the correct port was identified, test sequences sent to the phone generated responses confirming an active protocol layer awaiting commands.

The protocol employed two-letter command codes as its primary instruction format. A critical discovery emerged through error message analysis: invalid commands returned error responses that echoed the submitted command string. This behavior enabled systematic enumeration of the entire command space. Brute force testing of all 676 possible two-letter combinations (26 × 26) revealed 80 valid commands that returned non-error responses. Some commands demonstrated semantic meaning—such as SA for status queries—while others remained functionally ambiguous without additional context.

3.3 Persistence Problem and TCP Proxy Implementation

A significant obstacle emerged when credential settings written to the phone were stored only in temporary memory, disappearing upon device reboot. Initial attempts to solve this through three-letter command enumeration and semantically reasonable word testing yielded no results. The AI system proposed an alternative approach: implementing a TCP proxy on the Mac to intercept traffic between the Windows VM running proprietary software and the phone itself.

The proxy architecture positioned the Mac as a middleware layer that logged all communication while transparently forwarding packets between endpoints. This MITM configuration captured the complete command sequences used by the proprietary software, revealing protocol details invisible through external testing alone. The logged traffic exposed a TS command containing a binary payload with an embedded checksum mechanism that had prevented direct command replication.

3.4 Checksum Reverse Engineering and Algorithm Confirmation

Analysis of captured traffic identified the checksum as a single-byte value, making it computationally feasible to brute force all 256 possible values. Systematic testing revealed the encryption mechanism: a simple one-byte subtraction operation applied to the payload data. This discovery represented a breakthrough in protocol understanding, as checksums serve as integrity verification preventing unauthorized command modification.

The AI system employed closed-loop iteration to confirm the checksum algorithm, generating multiple test values and verifying that the discovered subtraction formula consistently produced valid checksums. This validation process demonstrated not merely pattern recognition but algorithmic understanding—the system could both identify the checksum format and programmatically generate correct values for arbitrary payloads.

With checksum generation solved, the complete protocol structure became accessible. The phone contained 256 bytes of persistent memory that could be directly manipulated through the appropriate command sequence. Once this memory structure was mapped, the team successfully performed a factory reset and programmed the device entirely through direct protocol commands, eliminating all dependency on the Windows VM and proprietary software.

4. Technical Insights

The reverse-engineered protocol architecture consists of two distinct layers: a command layer using two-letter ASCII codes and a data layer employing binary payloads with single-byte checksum validation. Of the 80 valid commands discovered through enumeration, critical functions include status queries (SA), temporary memory writes, and persistent memory programming (TS with checksum). The checksum algorithm applies simple subtraction: checksum = (sum_of_payload_bytes) % 256, inverted during validation.

The complete integration architecture flows: Viking phone → Twilio SIP trunk → 11 Labs conversational AI agent. This configuration enables the legacy VoIP device to interface with modern voice AI systems without modification to the phone's original hardware or firmware. The Twilio SIP trunk serves as a protocol bridge, translating between the Viking phone's proprietary VoIP implementation and standard SIP (Session Initiation Protocol) used by contemporary voice platforms.

Implementation considerations include Power over Ethernet requirements for phone operation, network topology constraints when using virtual machines with Wi-Fi bridging limitations, and the necessity of TCP proxy infrastructure for protocol analysis when manufacturer documentation is unavailable. The 256-byte persistent memory limitation constrains configuration complexity, requiring efficient encoding of SIP credentials and network parameters.

A significant trade-off emerges between using proprietary software with VM infrastructure versus direct protocol programming. While the former provides manufacturer-supported configuration interfaces, it introduces operating system dependencies and network bridging challenges. Direct protocol programming eliminates these dependencies but requires complete protocol understanding and custom tooling. The research team open-sourced their protocol implementation as a reusable skill for Claude Code, enabling others with Viking phones to bypass proprietary software entirely.

The methodology demonstrates extensibility beyond Viking phones to other legacy hardware systems. The systematic approach—port scanning, command enumeration, traffic interception, checksum analysis, and memory mapping—applies generically to undocumented protocols. Limitations include reliance on error message verbosity for command validation and the requirement for human physical interaction with hardware during testing phases.

5. Discussion

The findings illustrate a novel human-AI collaboration model where traditional role distributions are inverted. Rather than AI serving as a coding assistant for human-directed tasks, the AI system orchestrated the entire reverse engineering process intellectually while the human operator served as a physical execution agent. The researcher described this relationship explicitly: "I was actually the agent for Cloud. Cloud was orchestrating the whole thing... I was the hands for Cloud." This pattern suggests new frameworks for technical problem-solving where AI provides algorithmic strategy while humans contribute physical manipulation and sensory observation capabilities.

The research reveals that AI-assisted code generation tools can enable non-specialists to accomplish tasks traditionally requiring security engineering expertise. The researcher noted, "without Cloud code it wouldn't be possible to do that demo. It's not just it made it 10 times faster, it just made it possible because I'm not a security engineer whatsoever." This capability democratization has implications for hardware lifecycle management, enabling organizations to extend useful life of legacy equipment without specialized reverse engineering personnel.

Knowledge gaps remain regarding the scalability of this approach to more complex protocols with multi-byte checksums, encrypted payloads, or state-dependent command sequences. Future investigation should examine whether similar methodologies succeed with modern IoT devices employing cryptographic authentication or proprietary encryption schemes. Additionally, the legal and ethical dimensions of reverse engineering proprietary protocols warrant consideration, particularly regarding intellectual property rights and terms of service violations.

The work connects to broader industry trends toward hardware sustainability and right-to-repair advocacy. As manufacturers discontinue software support for functional hardware, reverse engineering capabilities become essential for preventing premature obsolescence. The open-sourcing of protocol implementations creates community knowledge repositories that reduce dependency on manufacturer cooperation for device longevity.

6. Conclusion

This research demonstrates that AI-assisted code generation tools enable systematic reverse engineering of undocumented legacy hardware protocols, even by practitioners without specialized security expertise. Through a combination of port scanning, brute force command enumeration, TCP proxy traffic interception, and iterative checksum analysis, the complete Viking VoIP phone protocol was decoded and documented. The discovery of 80 valid two-letter commands and a simple single-byte subtraction checksum enabled direct device programming without proprietary software dependencies.

The practical takeaway for hardware integration projects is that modern AI tools can orchestrate complex reverse engineering workflows that would otherwise require specialized knowledge. The human-AI collaboration model that emerged—with AI providing algorithmic orchestration and humans serving as physical execution agents—represents a replicable framework for tackling similar legacy hardware challenges. Organizations seeking to extend hardware lifecycles or integrate obsolete devices into modern infrastructure can apply this systematic methodology across various proprietary systems.

Future applications include extending this approach to other legacy hardware categories, developing generalized protocol discovery frameworks, and creating open-source protocol libraries that reduce redundant reverse engineering efforts across the technical community. The successful integration of a Windows XP-era VoIP phone with contemporary conversational AI infrastructure demonstrates that technological obsolescence is often a software rather than hardware limitation—one increasingly addressable through AI-assisted protocol discovery.

Sources

About the Author

Sean Weldon is an AI engineer and systems architect specializing in autonomous systems, agentic workflows, and applied machine learning. He builds production AI systems that automate complex business operations.

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