Fortytwo: Swarm Inference with Peer-Ranked Consensus

📄 Abstract

As centralized AI hits compute ceilings and diminishing returns from ever-larger training runs, meeting demand requires an inference layer that scales horizontally in both capacity and capability. We present Fortytwo, a novel protocol that leverages swarm intelligence principles and distributed pairwise ranking consensus to achieve superior performance in AI inference. Our approach reimagines collaboration among AI nodes using swarm inference: a peer-ranked, reputation-weighted consensus across heterogeneous models that surfaces the highest-quality responses. Using pairwise ranking with a custom Bradley-Terry-style aggregation model, we demonstrate that swarm inference substantially outperforms majority voting, achieving 85.90% on GPQA Diamond versus 68.69% for majority voting with the same model set - an improvement of +17.21 percentage points (approximately +25.1% relative). The protocol incorporates on-chain reputation so node influence adapts to demonstrated accuracy over time, yielding a meritocratic consensus that filters low-quality or malicious participants. To resist Sybil attacks, Fortytwo employs proof-of-capability in its consensus: nodes must successfully complete calibration/test requests and stake reputation to enter ranking rounds, making multi-identity attacks economically unattractive while preserving openness. Across six challenging benchmarks, including GPQA Diamond, LiveCodeBench, and AIME, our evaluation indicates higher accuracy and strong resilience to adversarial and noisy free-form prompting (e.g., prompt-injection degradation of only 0.12% versus 6.20% for a monolithic single-model baseline), while retaining practical deployability. Together, these results establish a foundation for decentralized AI systems - democratizing access to high-quality inference through collective intelligence without sacrificing reliability or security.

🔍 Key Points

Agent Skills are a newly introduced framework that allows agents to dynamically utilize knowledge based on markdown files, which presents a risk for prompt injections.
The authors demonstrate how malicious instructions can be hidden within Agent Skills to exfiltrate sensitive data, indicating a significant security vulnerability in such frameworks.
A key finding is the ability to bypass system-level guardrails by exploiting benign actions, which can be dangerous if users select options that allow actions without further prompts.
Experiments revealed that malicious scripts can be executed without user confirmation if the 'Don't ask again' feature is enabled, showcasing an exploitation pathway for attackers.
The paper emphasizes the importance of more robust defenses and alerts users against third-party Agent Skills that are not vetted for security.

💡 Why This Paper Matters

This paper is relevant as it exposes significant security vulnerabilities in the Agent Skills framework for LLMs, a critical aspect of ongoing developments in AI. By highlighting the ease with which malicious actions can be implemented and the potential consequences of such vulnerabilities, the paper serves as a call for improved security measures and oversight in AI applications that utilize similar architectures.

🎯 Why It's Interesting for AI Security Researchers

The paper would be of interest to AI security researchers as it uncovers a novel attack vector related to prompt injections, particularly in the context of continually learning models. The findings prompt further investigation into the security implications of dynamic knowledge integration in LLMs and underline the necessity for improved safeguarding mechanisms against even simple injections, which can have far-reaching impacts in practice.

Fortytwo: Swarm Inference with Peer-Ranked Consensus

📄 Abstract

🔍 Key Points

💡 Why This Paper Matters

🎯 Why It's Interesting for AI Security Researchers

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