In a local-consensus system like TrustMesh, the cost of an attack is inherently difficult to quantify precisely, because each node maintains its own perspective and trust evaluation. There is no single global metric that can be universally priced or slashed.
A useful analogy is BitTorrent’s tit-for-tat mechanism: reputation is derived from direct, observed interactions between peers, rather than from an externally verifiable stake. Because trust is based on firsthand behavior, it is not something that can be cheaply fabricated or globally asserted.
More concrete cost estimates therefore depend on the specific reputation rules implemented by each node. Those rules determine how quickly trust can be accumulated or lost, but that is an engineering-level design choice rather than a fixed protocol constant.
Regarding the first question, the worst-case scenario would be one in which an attacker successfully captures the view of all nodes. In that case, the winning proposals would be entirely controlled by the attacker, representing a total breakdown of honest influence.
A more realistic and weaker adversarial scenario is one where the attacker becomes a high-reputation node in the views of a majority of participants. Under such conditions, the attacker could bias the selection of winning proposals to extract block rewards, or deliberately slow down convergence by disrupting the scoring dynamics, thereby delaying finality.
From an end-user perspective, the most visible effect in such scenarios would be significantly slower finality. Additionally, if the round progression mechanism is poorly designed, different nodes may temporarily select different winning proposals, leading to inconsistent local outcomes across the network.
However, even in this scenario, it remains difficult for the attacker to completely destroy finality. As long as honest nodes remain mutually connected, proposal scores among honest participants continue to increase monotonically, and the gap between competing proposals continues to widen over time. This makes permanent stagnation hard to sustain.
Once the attack subsides, the network is expected to gradually self-heal: honest connectivity reasserts itself, local views realign, and a healthy topology eventually re-emerges without requiring explicit coordination or intervention.
Whether all attacks necessarily burn reputation is a very important question. My honest answer is: it is uncertain. In TrustMesh, reputation rules are explicitly designed to follow the objectives of the network rather than being fixed protocol constants, which means that this class of vulnerability cannot be categorically ruled out.
In practical engineering terms, two aspects need to be distinguished. First, low-visibility attacks are likely to exist and are fundamentally unavoidable. For example, an attacker may subtly bias consensus by assigning slightly higher scores to certain proposals. In a system like TrustMesh, which is intentionally noisy and decentralized, it is impossible to precisely detect such marginal bias. Instead, the system relies on the scale of reputation tables and diversity of local views to dilute these effects until they become negligible.
Second, TrustMesh was designed from the outset with the assumption that not all misbehavior can be cleanly attributed to a specific node. As a result, an implicit design principle is that all meaningful information must be verifiably attributable to an identifiable origin. Information whose origin cannot be verified is treated as spam, and information originating from unknown or untrusted parties may be selectively ignored.
This design choice does not eliminate all forms of abuse—denial-of-service attacks, in particular, remain difficult to prevent—but it limits the ability of attackers to accumulate influence through unattributable or unaccountable actions.