We are excited to be publishing, along with P2P, an in-depth research article on Restaking Networks Slashing Risk evaluation!
In this article, we introduced a protocol risk evaluation approach designed for restaking portfolio management on any restaking platform. This methodology isn’t limited to restaking — any staking protocol can be assessed in the same way. Unlike most existing approaches, we focus on fundamental analysis of a protocol's slashing risk. To illustrate this, we evaluate six networks and explore how this framework can be applied to portfolio construction from a restaker's perspective.
Worth noting that we’ve stuck to Symbiotic's “network”, for notation of protocols using stake for cryptoeconomic security, despite various platforms using different acronyms to describe the same kind of concept. For example, EigenLayer uses Actively Validated Services (AVS), Babylon uses Bitcoin Validated Services (BCN), Jito uses Node Consensus Networks (NCN), etc. We think "network" is the best fit, since the framework we’ve built can be applied beyond the restaking ecosystem.
Check the full article on HackMD and our Twitter thread for a succinct overview:
Thanks to dapplion, Maxim Merkulov, Mikhail Kalinin, and P2P.org's Ethereum team for reviewing and providing valuable feedback.
A short summary with the top 5 insights:
1. There Is No Industry Standard for Evaluating Network Risk
Despite the growing financial and technical complexity of restaking, the industry lacks a unified methodology for assessing slashing risk at the protocol level. Existing approaches largely focus on validator behavior, ignoring the nuanced and sometimes systemic risks embedded in networks’ designs. This paper proposes a structured, first-principles framework that fills this gap by evaluating risks rooted in the architecture, consensus design, and operational characteristics of networks themselves—providing a basis for protocol-level risk assessment.
2. Statistical/Stochastic Models Alone Are Insufficient for Slashing Risk Estimation
Slashing events are rare, inconsistent, and partially influenced by idiosyncratic protocol design flaws, making purely empirical, data-driven, assumption-based approaches unreliable. Historical data is either too sparse or not transferable across network architectures. The paper advocates for fundamental infrastructure assessment as essential not only in the absence of data, but also as a long-term anchor for modeling stochastic risk estimations.
3. A Structured, Weighted Scoring Framework Makes Risk More Quantifiable
To enable meaningful evaluation and comparison, we introduce a scoring system built around seven weighted metrics: Execution Architecture, Consensus Design, Slashing Conditions, Security Audits, Code Complexity, Maturity, and Reputation. Each network is assigned a score across these dimensions, calibrated by weighting and confidence levels that reflect the importance and availability of information. The resulting composite score (R) captures both the expected risk of slashing and the uncertainty of the evaluation itself, allowing restakers and researchers to rank networks based on risk exposure and make more informed, portfolio-aware decisions.
4. Unified Metrics Enable Comparability Across Diverse Network Categories
The framework’s power lies in its ability to normalize risk evaluation across distinct network categories—DA, Oracles, ZK, DePIN, AI, and Interop. By abstracting protocol-specific features into universal metrics and scoring them within a consistent structure, the framework enables side-by-side comparison of otherwise incomparable protocols, making it an effective tool for restakers managing diversified exposure across different networks.
5. The Composite Risk Score Serves as a Proxy for Expected Stake Loss
The final output of the framework—the network risk score —functions as a proxy for expected slashing loss and is designed to be actionable: can be plugged into yield/risk optimization models, used within a broader portfolio-based decision model, or on further research into network covariance and cascading risks. As richer restaking-related data becomes available, this framework will support dynamic recalibration while remaining grounded in first-principles logic.
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