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 "name": "Attack Specific Reputation Design",
 "text": "Threat resistance belongs inside the specification of a reputation system. Hoffman, Zage, and Nita-Rotaru decompose a reputation system into formulation, calculation, and dissemination components, classify attack classes, and use that decomposition to identify the design choices each attack exploits. This method links an attack class to a defense rather than treating a reputation score as self-validating.\n\nTheir survey identifies a specific composite mechanism. The attack is composite. One actor can acquire multiple identities and collude through them even when source data is authenticated. The colluding identities can participate mutually in events that generate real feedback at a rate that improves their scores faster than honest participation. This mechanism closely corresponds to wash interaction because the events are produced to manufacture feedback rather than to evidence independent service quality. Whitewashing permits an attacker to abandon a degraded identity and re-enter with a fresh reputation. Orchestrated attacks combine these strategies and change roles over time.\n\nThe source is a peer-reviewed survey of peer-to-peer reputation systems, and it does not study autonomous agent markets, measure attack prevalence, or test a particular production system. Its taxonomy predates blockchain wash trading terminology and does not analyze reputation transfer between task domains. Reputation is defined for a given domain and purpose, which supports contextual specification but does not establish that a score safe in one context can be carried safely into another. No surveyed system defends against every catalogued attack.\n\nThe design consequence is narrower than a general demand for a secure score. A scoring rule should state the attacker identities it permits, the interactions it treats as independent, the coalitions it assumes, the cost of re-entry, and the contexts across which history may travel. It should map each assumption to a tested defense and identify uncovered attack classes. Authentication alone cannot supply this result because authenticated identities can still collude and create feedback. A system that omits these bounds publishes a number without describing how adversaries may manufacture it.",
 "author": {
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  "name": "Wulf A. Kaal",
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  "The analyzed systems are primarily peer-to-peer reputation systems rather than autonomous agent marketplaces or sovereign local agent runtimes.",
  "The author-hosted manuscript carries an ACM Computing Surveys prepublication footer dated 2007, while the DOI-bound final journal record was published in 2009.",
  "The source predates blockchain wash-trading terminology. Its synthetic mutual-event mechanism corresponds to wash interaction, but the source does not use that modern label.",
  "The source defines reputation within a given domain and purpose but does not analyze cross-context reputation transfer or laundering.",
  "The source reports that no surveyed system defends against all catalogued attacks, so it does not establish a complete defense for the claim's entire attack set.",
  "The surveyed defenses may not cover later blockchain-specific or autonomous-agent attack variants."
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  "name": "A Survey of Attack and Defense Techniques for Reputation Systems",
  "url": "https://doi.org/10.1145/1592451.1592452"
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  "citation": "Wulf A. Kaal, Institutional Requirements for Sovereign Local Agent Runtimes (2026). SSRN: https://ssrn.com/abstract=7314479",
  "paper": "Wulf A. Kaal, Institutional Requirements for Sovereign Local Agent Runtimes",
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  "year": "2026",
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    "text": "Reputation allows parties to build trust, or the degree to which one party has confidence in another within the context of a given purpose or decision.",
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    "text": "However, even if source data is authenticated using cryptographic mechanisms, self-promotion attacks are possible if disparate identities or a single physical identity acquiring multiple identities through a Sybil attack [Douceur 2002] collude to promote each other. Systems that do not require participants to provide proof of interactions which result in positive reputations are particularly vulnerable to this attack. To perform the attack, colluding identities mutually participate in events that generate real feedback, resulting in high volumes of positive feedback for the colluding participants.",
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    "text": "Whitewashing attacks occur when attackers abuse the system for short-term gains by letting their reputation degrade and then escape the consequences of abusing the system by using some system vulnerability to repair their reputation. Often attackers will attempt to re-enter the system with a new identity and a fresh reputation [Lai et al. 2003].",
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