Spam has long been a nuisance in blockchain networks, clogging transaction queues and driving up fees. A new research paper from Delft University of Technology introduces a decentralized solution called STARVESPAM that could help nodes in permissionless blockchains block spam without relying on central control or costly fee mechanisms.
When blockchain networks get flooded
In open networks like Ethereum, Bitcoin, and Solana, anyone can send transactions as long as they pay the required fee. This openness also makes it easy for attackers to flood the network with low-value or junk transactions. These floods slow down processing, inflate fees, and sometimes even halt block production.
One well-known example occurred in 2022 when Solana went offline for seven hours after bots submitted millions of NFT minting transactions per second. Ethereum and Bitcoin have also seen repeated spam events that clogged memory pools and raised transaction costs for ordinary users.
The researchers set out to build a system that can distinguish spam from legitimate traffic using behavioral signals rather than transaction fees or centralized filters. Their approach is based on local reputation instead of global consensus.
How STARVESPAM changes the rules
STARVESPAM operates at the transaction relay layer, where nodes share transactions with each other before they are added to blocks. Each node monitors the behavior of its peers over time and assigns a reputation score based on factors like transaction rate, duplication, failure rate, and fee patterns.
When a node sees signs of abuse, it lowers that peer’s reputation score. Peers with low scores are throttled or temporarily ignored, while those with better reputations get normal access to resources. The system does not ban nodes outright but reduces their ability to consume bandwidth or clog memory pools.
Because reputation is local, each node makes its own decisions without waiting for the rest of the network to agree. This design avoids the need for protocol changes or central databases. It also means that spammers cannot regain access simply by creating new accounts, since new identities start with neutral reputation and must build trust over time.
Rowdy Chotkan, a co-author of the research, said the team wanted to make adoption feasible without disrupting existing protocols. “Adoption of STARVESPAM faces challenges, especially in environments where profit incentives dominate,” he said. “Validators may have little motivation to filter transactions if doing so reduces potential fee revenue, and relayers may prioritize throughput over quality control.”
Chotkan added that the system’s incremental deployability could help address this. “STARVESPAM does not require consensus modifications or network-wide coordination,” he said. “It is feasible for more conservative or high-reliability nodes, such as public RPC providers or gateways, to experiment with local filtering policies without affecting consensus or liveness.”
He noted that adoption will likely depend on how operators view the trade-off between short-term gains and long-term reliability. “Widespread adoption may depend on whether enough infrastructure operators see value in curbing spam for the sake of system health, user fairness, or operational cost,” Chotkan said. “People will start caring about this once large-scale spam attacks become more frequent.”
Overview of STARVESPAM’s transaction pipeline (Source: Research paper)
Testing STARVESPAM in the wild
To see how well the system works, the researchers replayed data from a major Ethereum spam event: the Otherside NFT mint of April 2022. During that event, gas fees surged above $180 million and many legitimate transactions failed due to network congestion.
The team compared STARVESPAM with several common approaches, including fee-based filters and rule-based systems like BanMan. In tests using 50,000 transactions, STARVESPAM blocked about 95 percent of spam while dropping only about 3 percent of honest transactions. Other methods either let too much spam through or excluded more legitimate traffic.
The researchers also simulated a network of 100 nodes to see how reputation evolved over time. Honest nodes quickly gained high scores, while malicious ones lost theirs. When some nodes switched from spammy to normal behavior, their reputation gradually recovered. This shows that the system can adapt without permanently excluding users who improve their behavior.
The study modeled how local filtering affects network-wide spam propagation. With no filtering, spam reached nearly every node. With STARVESPAM, most spam stopped after a few hops while honest transactions still reached most of the network. This suggests that local reputation could sharply reduce the spread of unwanted traffic without hurting normal activity.
Extending reputation across chains
The researchers see future versions of STARVESPAM adapting to multi-chain and Layer 2 systems. Chotkan said those environments bring new difficulties. “Cross-chain and Layer 2 environments introduce a new class of challenges for spam mitigation,” he said. “Attackers can exploit bridges, airdrops, and incentive programs to spam across multiple chains using coordinated Sybil identities. This makes local reputation harder to track, especially when activity is fragmented across different layers or domains.”
One potential solution involves extending reputation tracking across networks in a privacy-conscious way. “One promising direction is the use of shared identity primitives, such as decentralized identifiers or zero-knowledge credentials, to bind behavioral history across chains,” Chotkan said. “Integrating off-chain signals from indexers, RPC providers, or bridge monitors could also help nodes assess peer reputation even when traffic spans multiple domains.”
Why reputation beats reaction
The key innovation in STARVESPAM is its emphasis on behavioral accountability. Traditional blockchain defenses rely on economic deterrence, such as requiring higher fees or deposits. Those methods are easy to implement but often punish legitimate users along with attackers. A reputation system can adapt based on observed conduct instead of fee size.
The approach also avoids the fragility of static rules. Nodes can adjust thresholds and heuristics to match their hardware limits and risk tolerance. While the prototype uses simple rule-based scoring, the design could later incorporate machine learning or external reputation sources without changing its structure.
