Morality and Ethics of Autonomous Agents in Blockchain Systems

[Note: Below is a new paper discussing the Morality and Ethics of Autonomous Agents in Blockchain Systems. How much of it was generated by an agent?]

Abstract

Autonomous agents — notably Maximal Extractable Value (MEV) bots on decentralized exchanges and oracle-manipulating bots — have become entrenched actors in blockchain ecosystems. These bots leverage the transparent and permissionless nature of blockchains to reorder transactions or exploit data feeds for profit. Their actions can improve market efficiency by rapidly arbitraging price differences and providing liquidity, but they also raise serious ethical and systemic concerns. This paper offers a technical and rigorous analysis of the dual-edged impact of such agents across multiple blockchain networks. We explore how MEV bots can enhance liquidity and price accuracy while simultaneously imposing hidden costs (like increased transaction fees, unfair trade execution, and systemic centralization pressures). Similarly, oracle-exploiting bots that take advantage of pricing inefficiencies highlight the fragility of smart contract dependencies on off-chain data, sometimes leading to outright manipulation and user harm. A balanced evaluation is presented: we categorize beneficial versus malicious bot behavior and dissect their moral implications under a technology-focused lens. We also propose frameworks for more ethically aligned autonomous agent design, including protocol-level mitigations (fair transaction ordering, cryptographic protection of mempools) and improved oracle architectures. By examining these issues across Ethereum and other chains (e.g. Binance Smart Chain, Solana), the paper outlines a path toward aligning autonomous agents with the broader values of fairness, transparency, and trust in decentralized finance.

Introduction

Blockchain systems increasingly host autonomous agents — programs operating without direct human intervention — that interact with smart contracts and network protocols. In decentralized finance (DeFi), these agents often take the form of MEV bots and oracle bots. MEV bots are designed to capture Maximal Extractable Value, which refers to the profit obtainable by optimally ordering, inserting, or censoring transactions within a block. For example, a bot might detect a lucrative trade in the transaction mempool (the pool of pending transactions) and reorder or sandwich its own transactions around it to capture arbitrage profit. Oracle bots, on the other hand, monitor and manipulate data feeds — for instance, by exploiting price discrepancies or update delays in blockchain oracles — to gain an economic edge. Together, these autonomous agents form an algorithmic “dark forest” within blockchain networks, engaging in complex strategies that challenge conventional notions of fairness and ethics in financial systems.

The morality of these bots is hotly debated. On one hand, proponents argue that arbitrage and automated trading bots play a legitimate role in improving market efficiency. Indeed, arbitrage bots often equalize asset prices across exchanges (decentralized or centralized), ensuring that no large price disparities persist. Liquidator bots in lending protocols similarly perform a necessary function by closing under-collateralized loans, thereby preventing systemic insolvency. In these ways, certain autonomous agents can be seen as providing positive externalities — they keep decentralized markets aligned with global prices and maintain protocol stability. On the other hand, many of these bots employ tactics analogous to practices that are illegal or unethical in traditional markets, such as front-running orders or manipulating prices. Blockchain users may find their transactions mysteriously yielding worse outcomes due to an unseen bot taking advantage of them; for instance, a user might receive significantly fewer tokens in a trade than expected because a sandwich bot inflated the price just beforehand. Critics argue that such behavior undermines the fairness and open ethos of decentralized systems, effectively preying on uninformed or slower participants. Regulators and researchers have noted that MEV bots “engage in activities that would be illegal in traditional markets such as front-running and sandwich trades”, with parallels drawn to broker front-running or insider trading. This tension between efficiency and fairness forms the core ethical dilemma addressed in this paper.

Our goal is to provide a comprehensive, technically rigorous analysis of these issues, treating the blockchain as an evolving socio-technical system. We examine how MEV bots and oracle-exploiting agents operate, why their behavior emerges from the permissionless design of blockchain protocols, and what consequences (both beneficial and detrimental) arise at the individual and systemic level. Importantly, this analysis spans multiple blockchain networks. While Ethereum’s ecosystem (e.g. Uniswap, SushiSwap, Chainlink oracles) provides the most well-studied examples, similar bots and dynamics exist on Binance Smart Chain, Solana, Polygon, and other smart contract platforms. Each blockchain’s design — from consensus mechanisms to mempool transparency — influences the prevalence and impact of autonomous agents, so a cross-chain perspective is essential. By maintaining a broad technology-focused ethical perspective, we avoid alignment with any single philosophical doctrine, instead grounding the discussion in the practical realities of decentralized systems and the design choices that could steer them toward more ethical behavior.

The remainder of this paper is structured as follows: First, we outline our methodology for studying autonomous agents in blockchain environments. Next, in the analysis section, we delve into the mechanics of MEV bots on decentralized exchanges and oracle-manipulating bots, highlighting both their positive contributions and negative externalities. We then discuss the moral and ethical implications of these findings, considering analogies to traditional financial ethics and exploring emerging solutions or frameworks for improvement. Finally, we conclude with reflections on how blockchain networks might evolve to foster a healthier balance between algorithmic efficiency and ethical integrity.

Methodology

This research adopts a multi-faceted methodology combining literature review, case study analysis, and cross-chain technical comparison. We began with an extensive survey of academic papers, whitepapers, and technical documentation relating to MEV (Maximal Extractable Value) and oracle exploits in blockchain systems. Notable sources include peer-reviewed studies on MEV mitigation, blockchain policy analyses, and official documentation from projects like Ethereum (for MEV-Boost/PBS) and Chainlink (for oracles). For up-to-date insights, we also reviewed high-quality industry research blogs and reports (e.g. from blockchain analytics firms and foundation research notes), while consciously avoiding anecdotal social media content. The literature review helped establish definitions, typologies of bot behavior (such as arbitrage, front-running, sandwich attacks, and data manipulation techniques), and the known impacts on users and networks.

Subsequently, we conducted case studies of specific scenarios that illustrate the moral complexities of autonomous agents. For MEV bots, we examined documented incidents of sandwich attacks on Ethereum’s Uniswap exchange, replay (copy) attacks on pending transactions, and cross-DEX arbitrage events. One striking case involved MEV bots interacting with a DeFi exploit: when a hacker attempted to steal funds from a protocol, one bot tried to front-run the hacker to intercept the loot (a seemingly “ethical” act, albeit motivated by profit), while another bot assisted the hacker’s transaction in order to profit from the ensuing arbitrage opportunity. This exemplifies the unpredictable moral alignment of bots – sometimes acting as unintended white-hat agents, other times complicit in attacks for self-gain. For oracle-manipulation, we reviewed incidents where bots used flash loans to sway on-chain price oracles (leading to lending protocol breaches) and analyzed how oracle design weaknesses (e.g., slow update intervals or reliance on a single exchange’s price) have been exploited in practice.

Our analysis also explicitly compared different blockchain environments. Through technical documentation and research reports, we studied how Ethereum’s open mempool and gas auction system facilitates certain types of MEV, whereas alternatives like Solana have taken unique approaches to transaction ordering and MEV suppression. In one example, we reviewed the Solana Foundation’s decision in 2024 to penalize validators that were running private mempools enabling sandwich attacks. Similarly, we noted design differences in newer layer-2 networks and other L1 chains (e.g., differing consensus or auction mechanisms) that affect the prevalence of unethical bot behavior. By comparing these, we sought to identify whether certain architectural choices can inherently promote or discourage ethical outcomes.

Finally, to propose solutions, we synthesized recommendations from the literature (including cryptographic techniques, incentive redesign, and governance interventions) and evaluated their merits. Throughout our methodology, we focused on objective, technology-centric evidence of bot behavior and its consequences, rather than subjective moral judgments. This approach ensures that our ethical analysis is grounded in how these agents function and what effects they produce, allowing us to then rigorously assess those effects against principles of fairness, transparency, and welfare in the discussion section.

Analysis

Autonomous Agents on Decentralized Exchanges (MEV Bots)

On decentralized exchanges (DEXs) like Uniswap and SushiSwap, autonomous MEV bots actively monitor pending transactions and on-chain liquidity pools to exploit any opportunity for profit. These bots are essentially automated traders imbued with the special advantage of observing the transaction order flow in real time, thanks to the public mempool. Technically, whenever a user submits a DEX trade, it gets broadcast to the mempool before confirmation. MEV bots subscribe to this mempool feed, running algorithms to simulate the impact of each pending trade. If a potential profit is detected — for example, a large swap that will move the price of an asset — the bot can react within seconds by injecting its own transactions into the queue. The most common strategies executed by these DEX bots include arbitrage, front-running, and sandwich attacks, each with different implications for market efficiency and user fairness.

Arbitrage and Liquidity – A foundational activity for MEV agents is arbitrage between decentralized exchanges. Arbitrage bots seek price discrepancies for the same token across different liquidity pools or exchanges. If Token X is trading for a higher price on Uniswap than on SushiSwap, an arbitrage bot can buy low on the cheaper market and sell high on the expensive market, netting a nearly risk-free profit. In doing so, the bot is actually performing a useful service: it pushes prices back into alignment between markets, which enhances overall efficiency. Empirical analyses have noted that arbitrage “helps to promote market efficiency and is typically considered benign”. This is because arbitrage does not directly take value from other participants beyond correcting mispricings — traders on each exchange get the fair market price sooner than they otherwise would. Moreover, arbitrage bots contribute to liquidity by actively trading; they often soak up excess imbalance in one pool and release it in another, which can tighten spreads. Importantly, arbitrage opportunities in DeFi are accessible to anyone (in theory), which means this form of MEV is competitive and tends to have profits bid down over time to just cover costs (similar to arbitrage in traditional markets). As a result, many in the blockchain community view pure arbitrage bots as ethically neutral or even positive agents of market discipline.

Another class of beneficial agents on DEXs are liquidation bots, which operate in lending protocols but often interact with DEX liquidity for execution. When a borrower’s collateral value falls below required levels, anyone can trigger a liquidation: typically this involves using a DEX to swap the collateral for the owed asset, repaying the loan and taking a bonus. MEV bots avidly compete for these opportunities because liquidations offer rewards (a bonus or discount on collateral) for whoever acts first. While competitive, liquidations are a necessary mechanism to keep lending platforms solvent. By promptly liquidating risky loans, these bots prevent the accumulation of bad debt in the system. In fact, they are often the only entities fast enough to respond in time during volatile market conditions. Thus, liquidation bots can be seen as “automatic stabilizers” for DeFi money markets. The ethical dimension here is more straightforward: the user being liquidated is in that situation due to market conditions and protocol rules (not directly because of the bot), and the bot is performing a role the protocol explicitly incentivizes for the health of the platform. Research confirms that liquidations and arbitrage generally provide a net benefit to the ecosystem: arbitrage keeps DEX prices accurate, and liquidations prevent insolvency cascades. These forms of MEV extraction do not exploit users so much as execute the rules of the system, so they are often classified as non-toxic MEV or “benign” behavior.

Front-Running and Sandwich Attacks – In contrast to the above, other strategies target users’ trades to extract value at the users’ expense. The simplest is front-running, where a bot seeing a large pending order will immediately place its own order to buy the same asset (driving the price up), anticipating that the user’s buy will come next and further push the price, allowing the bot to sell at a profit. The user in this scenario experiences slippage – the difference between the expected price and the executed price – which ends up worse because the bot traded first. A more complex (and notorious) variant is the sandwich attack, which combines a front-run and a back-run around a single victim transaction. In a sandwich attack, the MEV bot identifies a target trade in the mempool (usually a large swap that will significantly move the price on a DEX). The bot then inserts two transactions: one just before the victim’s trade and one just after. The pre-trade is a buy (if the victim is buying, or a sell if the victim is selling) that pushes the price in a direction unfavorable to the victim. The victim’s trade executes next, at the distorted price. Immediately after, the bot’s second transaction executes, which is the opposite trade (sell or buy) to pocket the price difference created by the victim’s activity. The bot’s rapid one-two trade yields it a profit essentially harvested from the victim’s increased costs. The victim receives a worse exchange rate than they would have in the absence of the bot — effectively a hidden fee taken by the attacker.

Figure 1: Illustration of a Sandwich Attack. The mempool on the left shows a target user transaction (green) identified by an MEV bot. The bot inserts one transaction before and one after the target. In the proposed block (right), the bot’s first transaction (red) executes right before the victim (green), causing a less favorable price, and the second red transaction executes immediately after, netting profit from the price swing. Such ordering manipulation is orchestrated by block proposers or miners who accept the bot’s higher gas bids to prioritize its transactions.

From a moral standpoint, sandwich attacks and related front-running tactics are widely viewed as predatory. They create no value for the overall market – unlike arbitrage, they do not correct a price discrepancy between markets; unlike liquidations, they do not shore up a protocol. Instead, they exploit a structural loophole: the public visibility of pending transactions. In traditional finance, trading on advanced knowledge of a large pending order (especially if obtained through privileged access) is considered unethical and is often illegal (e.g., broker front-running). On blockchains, the information is public to all, which complicates the analogy – the bot operator isn’t an “insider” in the classic sense since anyone could observe the mempool. Nevertheless, the effect is the same: the original trader is worse off because someone else used timing advantages to step in mid-process. The Bank for International Settlements noted that MEV of this sort “can hence resemble illegal front-running by brokers in traditional markets”, effectively acting as an “invisible tax” on regular users’ transactions. Indeed, the profit that the sandwich bot earns comes directly from the victim’s loss (in terms of higher cost), so it’s a zero-sum extraction. We might compare it to a shopper who announces they will buy a large quantity of a commodity, only for a speculator to rush in, buy up the stock, and sell it to that shopper at a marked-up price – all in the span of seconds. Few would argue that is fair behavior; yet on-chain, this is simply the outcome of transparent markets and rational, if ruthless, automation.

The prevalence of these attacks is not trivial. Measurements on Ethereum have shown significant cumulative losses for users due to sandwich attacks. In one 30-day period in 2024, sandwich bots on Ethereum collectively extracted around $1.2 million in profits (which corresponds to user losses of a similar magnitude). In times of market volatility, the frequency can spike, as big swings attract more bot activity. It’s important to note that miners/validators themselves may execute these strategies or more commonly, accept bribes (high gas fees or explicit payments) from bots to include their transactions first. This dynamic set off a kind of arms race in Ethereum’s mempool: multiple bots might detect the same opportunity and bid up the gas price or priority fee they attach to outrun each other, leading to gas wars. During intense periods, users have observed transaction fees skyrocketing well above normal levels because bots are competitively bidding for block space to execute a lucrative MEV strategy. These gas bidding wars are another negative externality: they congest the network and price out regular users from making transactions, undermining the blockchain’s usability. In summary, toxic MEV strategies like front-running and sandwiches degrade user trust and fairness. They amount to an ethics violation in the eyes of many, as they systematically take advantage of those who lack the tools or knowledge to defend their transactions.

Systemic Consequences on DEX Ecosystems – Beyond individual trades, the rampant competition of MEV bots can have broader systemic impacts. One concern is the centralization of power in the hands of those who control MEV extraction. If certain bots or pools of miners become exceptionally proficient at capturing MEV, they gain outsized profits. In proof-of-work settings, this could enable those miners to invest in more hardware, further dominating block production. In proof-of-stake, validators earning more from MEV could compound their stake faster. This rich-get-richer feedback loop tends to concentrate influence, which is contrary to the decentralization ethos. It was observed that under proof-of-work on Ethereum, mining pools became highly specialized in MEV, leading to an uncomfortable centralization of what should be a decentralized function. Ethereum’s move to proof-of-stake and the rise of third-party block builders (via Flashbots) was in part an effort to level this playing field, as we discuss later. Nonetheless, the risk remains that a few actors (or cartel) could control most MEV, effectively acting as invisible intermediaries extracting rents from all transactions.

Another systemic issue is the potential for consensus instability. If MEV rewards in a given block become extremely high (for instance, an unusually large arbitrage or liquidation), there is an incentive for miners/validators to reorg the chain – i.e., discard or replace previous blocks – to capture that value. This concept, highlighted in the “Time Bandit attacks”, means consensus can be threatened by economic incentives to rewrite history for profit. While such attacks are complex and rare, the mere possibility suggests that excessive MEV could put the integrity of the blockchain at risk if not properly managed. MEV has even been described as an existential threat to consensus security in some research, which raises deep ethical questions: should profit motives be allowed to override the fairness and finality of a supposedly neutral ledger?

In closing this subsection, we note that not all blockchains are equally susceptible to the types of DEX MEV described. Ethereum historically had an open mempool where these games played out vividly. Other networks have tried variations: for example, Binance Smart Chain (BSC), being Ethereum-compatible, similarly suffers from MEV bots, often the very same bots ported over (with sometimes even more ruthless efficiency due to faster block times). Solana, which uses a different high-performance design, initially did not have a public mempool in the same way — transactions are sent to a leader — yet MEV still emerged via private ordering. The next subsection will examine cross-chain differences and measures, but suffice to say that DEX-oriented bots have become a universal phenomenon wherever there is on-chain trading. The ethical concerns they pose — fairness, equality of opportunity, trust in markets — are now front and center in the DeFi discourse.

Oracle-Interacting Bots and Data Feed Exploitation

While MEV bots target the transaction ordering within a blockchain, another class of autonomous agents focuses on the interfaces between blockchains and external data – namely, oracles. Blockchain oracles are services that feed real-world data (like asset prices, event outcomes, or random numbers) into smart contracts. They are crucial for decentralized applications: for example, a lending platform needs a price oracle to know when collateral value has dropped, and a prediction market needs an oracle to settle bets on external events. However, this reliance introduces what’s known as the “oracle problem”: how to ensure the data on-chain is accurate and not manipulated. Autonomous bots have learned to exploit any weakness in oracle mechanisms or timing to extract profit, often causing harm to regular users and protocols in the process.

Exploiting Price Inefficiencies and Lags – One straightforward opportunity arises when there is a price discrepancy between an on-chain DEX price and an oracle-reported price. Many DeFi platforms use time-weighted averages or other smoothing techniques in their oracles to avoid instantaneous manipulation. This means there can be short windows where the oracle price for an asset lags behind the true market price. An autonomous agent can take advantage of this by trading in a way that forces a profit given the stale price. For instance, suppose a lending protocol allows borrowing based on an oracle price that updates every 10 minutes. If the market price of a token plummets suddenly, there is a brief period before the oracle reflects this drop. A bot could quickly borrow the maximum against that token’s outdated high price (perhaps using a flash loan to supply collateral), and then once the oracle updates and the collateral is insufficient, the position will be liquidated – but by that time the bot may have absconded with the borrowed funds, leaving the protocol with bad debt. Such a strategy is essentially using oracle latency to perform an economic exploit. It’s ethically problematic because it’s a form of gaming the system, akin to using inside information or a time gap to one’s advantage. The borrowers in these cases are not innocent users but typically the bots themselves creating sacrificial positions for profit, so the direct victims are the protocols and their stakeholders (e.g., other lenders who absorb losses).

Oracle Manipulation Attacks – Even more direct are attacks where the bot manipulates the data feed itself. This is usually feasible when a DeFi protocol uses a decentralized exchange price directly as an oracle (an on-chain oracle), or when an oracle aggregates data in a way that can be influenced on-chain. For example, early DeFi platforms sometimes used the price from a Uniswap pair as the source of truth. A bot could deploy capital (potentially via a flash loan) to trade on that pair aggressively, pushing the price far in one direction within a single block. The smart contract, reading the price at end-of-block, would then think the asset’s value is extremely low or high, leading to unintended outcomes (like massive liquidations or minting of tokens). The bot reverses the trades in the next block, returning the price to normal and repaying its flash loan, but not before causing the protocol to release funds that the bot pockets. Such oracle spoofing attacks have led to multi-million dollar losses in various protocols. They are essentially man-made market distortions executed faster than any human could manage, only possible because the contracts blindly trust the manipulated data. A notable feature is that these attacks often do not require breaking cryptographic security — they exploit economic design flaws. The morality of this is equivalent to deliberately triggering a faulty mechanism for gain; one could argue it’s closer to hacking or fraud than to legitimate trading. In interviews, security experts have pointed out that “oracles and bridges are two of the most significant vulnerabilities in the DeFi world today. Because if you can hack the oracle, you can change the outcome of actions driven by a smart contract… at a pure transactional level, or it could be at a whole blockchain level.”. In other words, compromising an oracle’s integrity can have sweeping consequences, from draining a single contract to destabilizing an entire ecosystem if that oracle is widely used.

One infamous example involved a DeFi lending platform where the price of a thinly traded asset was manipulated on a DEX by a flash-loan-fueled bot, causing the oracle to report an inflated price. The bot then borrowed heavily using that asset as collateral and promptly defaulted after siphoning the borrowed funds, because the collateral’s true value was a fraction of what the oracle briefly indicated. In this scenario, unsophisticated users are not directly involved or victimized as in sandwich attacks; instead, the victims are the protocol (and by extension its users who suffer the losses collectively). Ethically, this is akin to a financial hack: the bot exploited a known weakness (the protocol’s reliance on a single DEX price) in a way the protocol designers did not anticipate as a use-case. As one blockchain architect put it, “It’s not a hacking attack, it’s just a vulnerability… blockchain designers never thought that somebody might reorder the transactions (to exploit it)”. This quote underscores an important point: many of these bots operate in a gray area where they aren’t violating the rules of the code – they are using the code as written, but to unintended effect. This blurs the moral line between legitimate strategy and exploit.

Oracle as Single Point of Failure – The deeper ethical issue with oracle-exploiting bots is how they expose centralization or fragility in ostensibly decentralized systems. A truly decentralized financial system would ideally not have levers that a single actor (or a cartel) can pull to cause widespread damage. However, oracles by their nature collect off-chain information, and if a bot can control or heavily influence that feed, it becomes a centralized attack vector. Research by the FCA in 2024 highlighted that industry experts see oracles as introducing “a new dimension of risk” and in some cases acting as a “single point of failure,” since a savvy attacker can identify which oracle a smart contract relies on and then target it for manipulation. This is exactly what oracle-manipulation bots do. They turn the oracle into the system’s Achilles’ heel. The morality question here is often framed in terms of accountability: in a traditional financial system, if someone deliberately manipulated a benchmark (say, LIBOR or a stock price) to force a payoff elsewhere, that would clearly be unethical and likely criminal. In DeFi, who is accountable when a bot performs a similar manipulation? The bot’s owner is often pseudonymous and hard to trace; the miners/validators who included the bot’s transactions might be complicit only insofar as they picked the highest fees (not knowing the full intent); the protocol developers can be blamed for a poor design, but they did not perpetrate the act. This diffusion of responsibility is problematic. It means malicious actors can hide behind the faceless automation of bots and the permissionless participation in networks, making justice or restitution extremely difficult. It also raises trust issues: if users fear that any protocol could be gamed via its oracle, overall confidence in DeFi erodes.

Cross-Chain and Multi-Oracle Bots – Autonomous agents have also engaged in strategies that span multiple platforms and data sources. For example, some bots monitor price feeds on one chain (like a primary Oracle network) and trade on another chain’s DEX where the price hasn’t yet adjusted, thus performing cross-chain arbitrage. This can be positive in aligning prices across chains, but it can also be used to propagate shocks. If an oracle on one chain is manipulated, bots might carry that false price to another chain’s markets before it’s corrected. Additionally, as bridges connect ecosystems, bots may exploit bridge or oracle interactions (one high-profile case involved manipulating the price on one side of a bridge to steal funds from the other side, effectively tricking the bridging smart contract). These multi-domain exploits highlight that the ethical and technical challenges are not confined to single ecosystems; they are interlinked in the broader blockchain network-of-networks.

In summary, oracle-interacting bots present a somewhat different moral picture than DEX MEV bots. They tend to be unequivocally harmful in their direct actions (you’d be hard-pressed to find a positive interpretation of a bot that intentionally misleads an oracle for profit). At best, one might argue they expose weaknesses that can then be fixed, acting as involuntary “testers” of the system’s integrity. But that’s a silver lining argument; the immediate impact is negative. These bots profit from deception or distortion of truth, which cuts against the grain of transparent and trust-minimized finance. The presence of such bots signals that certain protocols haven’t achieved the level of trust decentralization they aimed for – if a price can be so easily skewed, was the system ever truly robust? For the users and communities affected, these events feel like thefts or attacks, not just market activity. Thus, the ethical consensus is that oracle manipulation is something to be actively guarded against, and doing so is necessary to uphold the integrity of decentralized finance.

Cross-Chain Perspectives and Comparisons

Different blockchain platforms have experienced the rise of autonomous trading and exploit bots in varying ways, depending largely on their protocol designs. It is instructive to compare how Ethereum versus other chains handle (or suffer from) the activities of MEV and oracle bots, as this sheds light on whether certain architectures are more ethically aligned by design or allow different balances of power between users and bots.

Ethereum – Ethereum (especially pre-2022, before significant mitigation measures) was often considered a “paradise” for MEV bots. The combination of a completely transparent mempool, high DeFi activity, and miners’ freedom to order transactions arbitrarily led to a flourishing of bot strategies. Studies found that at times one out of every 30 transactions in Ethereum was an MEV-motivated transaction inserted by a miner or bot. Ethereum’s switch from Proof-of-Work to Proof-of-Stake in 2022 did not eliminate MEV; it simply changed the actors from miners to validators. Recognizing the ethical and stability issues, the Ethereum community and researchers pushed for solutions like MEV auctions and Proposer-Builder Separation (PBS). With PBS, specialized third-party builders create MEV-optimized blocks and bid to have validators propose them, which outsources the heavy optimization to a few entities but at least makes the competition structured and returns a portion of profits to validators (and by extension to all stakers). This was a mixed development ethically: on one hand, it acknowledges MEV as inevitable and tries to distribute it more fairly (preventing only large validators from capturing it all). On the other hand, it formalizes the extraction of MEV rather than eliminating toxic behavior; sandwich attacks still happen, but via private “bundles” sent to builders (e.g., through Flashbots) rather than the public mempool. As of 2024, Flashbots and similar systems have indeed reduced public gas wars (benefiting users through lower gas spikes) and made MEV extraction more efficient, but they also introduced concerns about censorship (since these systems can choose which transactions to include or exclude, e.g., compliance with sanctions). Ethereum’s approach, therefore, has been to manage MEV’s downsides without destroying the profit motive. Ethically, it is a pragmatic stance: instead of declaring these bots “evil” (an unenforceable stance in a permissionless system), Ethereum is steering the competition in a direction that hopefully minimizes collateral damage to users (like front-run bots stealing less from users because user transactions can bypass the public mempool via Flashbots’ private relay). It is an ongoing experiment in incentive alignment.

Binance Smart Chain (BSC) – BSC is an Ethereum-like environment (EVM-compatible) but with a different consensus (Proof-of-Authority/Stake, with a limited set of validators). MEV bots on BSC operate much as they do on Ethereum, engaging in arbitrage and sandwich attacks on BSC’s popular DEXs (PancakeSwap, etc.). One might think the smaller validator set (21 validators) could collude or have an easier time controlling MEV, but in practice BSC’s rapid 3-second blocks mean bots had to be even faster and more competitive. If anything, the ethical landscape is similar, though BSC has seen less public discussion of MEV – possibly due to its more centralized nature, large extractors might keep it under wraps. The fundamental issues of fairness remain. The chain’s higher throughput did not eliminate bots, it just gave them more frequent block opportunities. Interestingly, because BSC is centralized in governance, in theory the chain maintainers could implement rules or measures against toxic MEV. However, we have not seen major moves like that publicly known, suggesting that as long as performance is good, the ethical considerations were secondary in BSC’s priorities.

Solana – Solana presents a very different design: it uses a unique timestamping (Proof-of-History) and a system where a validator (leader) has a short window to order transactions that are sent to it, rather than a public mempool where anyone can see and grab ordering priority. Early on, this led to speculation that Solana might be more resistant to MEV. In reality, MEV still exists on Solana (like DEX arbitrage, liquidations) but the sandwich/front-run type was less trivial because transactions go directly to the leader’s turbine propagation. However, over time, actors on Solana started using private mempools or exclusive arrangements with validators to achieve similar outcomes (e.g., sending transactions directly to a validator to front-run someone else, effectively making a covert channel). Recognizing the harm sandwich attacks could do, the Solana community took a bold approach in 2024: the Solana Foundation removed several validators from its delegation program (which delegators use to boost certain validators) because those validators were running private software that enabled sandwich attacks. In other words, the foundation identified behavior considered “harmful to the ecosystem” and punished it by revoking support. This centralized intervention in a “decentralized” network was controversial. It reflects a direct ethical stance: Solana’s leadership explicitly stated they are “not interested in retail users being robbed” by exploitative MEV. Further, the community via Jito (a Solana MEV infrastructure project) contemplated blacklisting validators who engage in harmful MEV. Proponents argue this improves user fairness on Solana and sets a norm that certain behavior crosses the line. Critics, however, worry that this hands too much power to central authorities (the Foundation or committee deciding what is a harmful behavior), possibly undermining trust in neutrality. Solana is also exploring technical solutions such as priority fee markets and in-protocol auctions to manage MEV more transparently. The Solana case is a fascinating study: it leans toward an ethics-by-governance model, where human governance steps in to enforce moral standards (at the risk of centralization), in contrast to Ethereum’s ethics-by-incentive-engineering model.

Other Chains (Polkadot, Avalanche, Cosmos, etc.) – Many newer chains are keenly aware of the MEV issue from the outset and have baked in some mitigations. For example, the Cosmos ecosystem (which includes chains like Osmosis, a DEX chain) has experimented with threshold encryption of mempool transactions. This means transactions are submitted encrypted and only reveal their contents after the block is finalized, preventing mempool sniping. Such approaches can effectively eliminate classic front-running because no one (not even the validator) knows the specifics of a trade until it’s too late to reorder it. However, these techniques can complicate the network protocol and have latency trade-offs. Polkadot’s paradigm allows parachains to design their own MEV mitigations (some might choose batch auctions or randomize inclusion). A noteworthy development is CowSwap on Ethereum (built on Gnosis protocol) which does batch auctions across a short timeframe and uses a solver competition to get users the best price, inherently protecting against sandwiches by executing all trades in a batch at a uniform clearing price. While not a “chain” per se, it’s an application-level solution that could inspire chain-level designs elsewhere.

In terms of oracle issues, Chainlink’s approach of aggregating many nodes and data sources per price feed has become standard across chains, which raises the bar for manipulation (though at a cost of some centralization of the oracle mechanism itself). Some chains like those in the Cosmos ecosystem allow use of multiple oracles or direct cross-chain queries, reducing reliance on a single oracle provider. Nonetheless, any system needing external data will always have to trust something, and bots will look for cracks (be it a low-reputation oracle node they can bribe or a governance mechanism they can Sybil-attack to influence an oracle’s settings.

Overall, the multi-chain perspective shows a spectrum of responses: from Ethereum’s market-driven approach to Solana’s governance-driven approach, and others trying purely technical fixes. The existence of these different models is heartening in that it allows experimentation. Ethically, one could argue that blockchains like Solana or Cosmos that proactively protect users from predatory bots are taking a more user-centric moral stance, whereas Ethereum’s neutral approach (historically “miner-centric”, now somewhat more user-considerate with PBS and private tx options) reflects a more laissez-faire or caveat emptor philosophy. Time will tell which approaches yield a more robust and fair ecosystem without compromising decentralization. What’s clear is that no major smart contract platform is ignoring the issue — the presence of autonomous agents and their ethical ramifications has become a key consideration in protocol design and upgrades.

Discussion

The analysis above paints a picture of autonomous blockchain agents as a double-edged sword: they are at once indispensable cogs in the decentralized machine and potential saboteurs of the very principles that machine purports to uphold. In this discussion, we step back to assess the moral and ethical landscape, and explore frameworks for aligning these agents with more ethical behavior. Several interrelated themes emerge: market fairness, transparency, consent, and the distribution of value and power. We discuss each in turn, followed by potential solutions ranging from technical interventions to governance and incentive reforms.

Fairness and the “Invisible Hand” of Code – Traditional financial ethics often revolve around fairness: fair access to markets, fair pricing, and preventing unfair advantage (as in prohibitions on insider trading or front-running by brokers). In blockchain networks, fairness is ostensibly guaranteed by open access and transparent rules. Anyone can participate, and all actions are governed by code that is applied equally to all. The reality, however, is that informational and technical asymmetries create an uneven playing field. MEV bots and oracle manipulators exploit those asymmetries — they have greater speed, more knowledge (via real-time monitoring or simulation), and greater sophistication than the average user. The ethical question arises: Is it fair to allow participants to gain at the expense of others solely by virtue of superior tech capabilities in a decentralized system? The libertarian viewpoint might answer “yes” — in a free system, skill and effort (in this case, coding skill and capital investment in bots) deserve reward, and users must educate themselves or use protective services. The counterpoint is that blockchains were supposed to democratize finance, not create a new breed of hyper-HFT insiders. When a normal user’s experience is consistently suboptimal because an unseen algorithmic middleman is taking a cut, that user will feel the system is rigged, much as retail traders in traditional markets felt when they learned about Wall Street’s high-frequency trading advantages.

The term “invisible tax” for MEV is apt — it’s a tax not decided by any public process, but extracted in the shadows of the mempool. For blockchain systems that aim to be self-governing, such invisible taxes present a legitimacy problem. If the community doesn’t consent to pay these “fees” to arbitrageurs and front-runners, should they be tolerated as a natural byproduct of free markets, or actively curbed? This is a moral stance each community has to take. Ethereum’s implicit stance for a long time was tolerance (with an eventual pivot to mitigation once the externalities grew too big), whereas other communities have sought prevention sooner.

Transparency vs Exploitation – One of the beautiful features of blockchain is transparency: anyone can audit the code and observe the transactions. Ironically, this transparency is what enables a great deal of MEV extraction. A user’s pending trade is transparently visible, allowing a bot to pounce on it. This creates a paradox: more privacy could mean less exploitation, but too much privacy undermines the open verifiability that blockchains champion. The ethics here revolve around the intent of transparency. Transparency is intended to hold actors accountable and level information access. But when transparency becomes a tool for predation (bots watching your every move), it backfires. This suggests that maybe users deserve a certain degree of privacy or at least tactical opacity (like the brief hiding of their transaction details until inclusion) to protect them, without compromising the overall auditability of the system. Solutions like encrypted mempools or commit-and-reveal schemes attempt to solve this, essentially delaying transparency just enough to foil front-runners while preserving eventual openness. Such approaches align with an ethical view that participants have a right to transact without being exploited for using the public channel. In practice, one might imagine something like: a user broadcasts a hash of their transaction (commit), miners order those commits (which reveal nothing exploitable), then reveal the actual trades after ordering is decided. This would be more fair, but requires significant changes to protocol or usage of specialized relays, and often comes with trade-offs in complexity or liveness.

Consent and Participation – Another ethical lens is the concept of consent. When users engage with DeFi, do they consent to these bots extracting value from them? One could argue that by using a public blockchain, a user impliedly consents to all that the protocol allows, including being front-run. This is akin to the legal argument noted in the BIS bulletin: “anyone who participates in such an ecosystem essentially accepts the rules encoded in its protocol… it is unclear whether a participant could object if someone exploits those rules to their advantage”. If the code permits it, you agreed to it by using the system. However, in practice most users have no idea of the intricacies of these “rules” — they just see a UI that promises a trade, and may not realize a bot can insert itself. Thus, the notion of informed consent is weak. Ethically, a system that surprises users with hidden adversaries is problematic. It suggests a need for either better user education (“you may get sandwiched, set your slippage tolerance carefully and consider protection”) or systemic changes that remove the need for users to understand these lurking dangers. One interesting development is user-facing tools that fight back, such as anti-MEV wallets or DEX aggregators that route orders in a protected manner (like CowSwap’s batch auction or MetaMask’s new settings to use private order flow). These give users a way to opt-out of being prey. From a consent perspective, that is empowering: users can choose to avoid the public mempool (hence opting out of exposing themselves to bots). The more such options become standard, the more we might see a bifurcation where only users who knowingly choose to trade raw in the public mempool will face those risks.

Distribution of Value and Power – The presence of autonomous agents raises the question: who ultimately benefits from their activity, and is that distribution of value aligned with the community’s values? In the case of benign arbitrage bots, one might say everyone indirectly benefits (through efficient markets), while the bot operator profits as a reward for providing a service. That seems acceptable. In the case of sandwich bots, only the bot operator benefits, while the user and possibly others lose out, which seems misaligned. When bot operators accumulate outsized profits (recall the $550M+ extracted on Ethereum over a couple of years by MEV tactics), they become a new class of insiders or rentiers in the ecosystem. This has prompted discussions on whether some of this value should be recaptured or redistributed. For example, protocols like KeeperDAO (now merged into Rook) tried to create a coordination mechanism where users and bots share the MEV: users let the protocol capture the MEV and then rebate some back to users. Another idea is MEV redistribution at the protocol level – Ethereum’s PBS does this partially by giving MEV to validators (and thus to all stakers), but that still doesn’t directly compensate the specific users who were victims of, say, a sandwich. It merely socializes it. A more radical idea is to redesign DeFi apps so that what would have been MEV profit is instead captured by the protocol itself for its treasury or users. For instance, a DEX could integrate an arbitrage bot in-house and use it to stabilize prices, funneling the gains to liquidity providers or all token holders rather than an external bot. This edges toward a collective ethics approach: instead of free-for-all competition where the quickest predator wins, the ecosystem as a whole agrees to internalize these gains for common good.

Legal and Regulatory Reflections – While this paper is technology-focused, it’s worth noting that legal frameworks are beginning to catch up. If a DeFi action by a bot mimics what would be illegal in traditional finance (market manipulation, insider trading, front-running), should it be prosecuted? Currently, enforcement is sparse due to anonymity and jurisdictional issues, but one can imagine future cases where regulators attempt to make examples of identifiable actors. Ethically, some argue that just because the blockchain allows an action doesn’t make it right; laws against fraud or manipulation exist for a reason and perhaps should extend into DeFi. Others counter that applying old rules to a radically transparent and self-custodial system is misguided – we need new paradigms. The IOSCO and other international bodies have indeed taken note of MEV as a potential concern for market integrity, but concrete guidelines are still forming. Meanwhile, within the communities, there’s talk of ethical codes of conduct for MEV – for example, the idea of “fair ordering protocols” being a norm. Some projects like Eden Network in the past tried to advertise themselves as providing an “MEV protection” service where only “good” MEV (like arbitrage) was allowed and not sandwiching, though these often rely on social consensus and can be circumvented by those not opting in.

Given these considerations, what frameworks or solutions can we propose for ethically aligned autonomous agents? We outline a few angles:

  • Transparent MEV and Arbitrage as a Public Good: Encourage frameworks where arbitrage and similar useful bot activities are performed in the open or by protocols themselves. For example, a DEX could have an integrated arbitrage system that automatically balances prices with other exchanges, capturing the value for its users. This leaves less meat on the bone for third-party bots to exploit, while improving efficiency. Bots could then shift focus to genuinely useful tasks (like filling large orders optimally, etc.) that help users.
  • Fair Transaction Ordering Mechanisms: At the consensus or mempool level, implement systems that neutralize the advantage of seeing others’ transactions. Ideas here include:
    • First-Come-First-Served Ordering: Some research prototypes enforce that transactions are ordered roughly in the order they were received by the network (with cryptographic proofs). This would prevent reordering for profit. The challenge is defining “receive order” in a decentralized way and dealing with network latency.
    • Batch Auctions: Group transactions within small time windows and execute them together. This is how CoW Protocol’s CowSwap works. In an auction, everyone effectively trades at the same price for that block, so even if a bot sees a trade, it can’t sandwich within that batch — it would just participate in clearing the same price. This sacrifices some real-time granularity for fairness.
    • Encrypted Mempools: As mentioned, encrypt transactions so bots can’t see them until a block is sealed. Projects like Shutter Network and proposals for Ethereum include this approach, using threshold cryptography.
    • Randomized Ordering or VRF (verifiable random functions): Randomly shuffle or use a lottery to decide ordering of transactions that came in the same block interval, so bots cannot consistently win just by being fast.

Each of these needs careful engineering to avoid new problems (like denial of service if an encrypted tx never reveals, or reduced efficiency). But ethically, they directly aim to protect users from unfair sequencing.

  • Oracle Robustness and Diversity: To tackle oracle manipulation, we need oracles that are hard to game. Some recommendations:
    • Use aggregated price feeds from multiple sources and over time. For instance, a combination of decentralized exchanges, centralized exchange APIs, and time-weighted averaging makes it much harder for a bot to spoof all inputs at once.
    • Implement circuit breakers: if a price moves too fast beyond normal market movement, the protocol could freeze actions for that asset temporarily. This needs to be done carefully to not introduce centralization, but some protocols have time-delays or administrator keys to pause if an oracle update seems malicious. A decentralized version could be programmed thresholds.
    • Emphasize oracle decentralization: encourage competition among oracle providers and perhaps have contracts take the median of several oracle networks. This way, an attacker must compromise many systems, not just one.
    • On the oracle networks themselves, use reputation and crypto-economic security so that feeding bad data (especially if detectable after the fact) leads to slashing or punishment. Chainlink, for example, has discussed explicit penalty and reputation systems for nodes that diverge from the consensus of prices, although currently the security comes mainly from the assumption that nodes want to keep earning fees (a reputational incentive).
    • Ultimately, reduce reliance on on-chain instantaneous prices for critical decisions. Designs like stable swaps (constant product AMMs) now often avoid using their own pool price for lending or other logic; instead, they plug into a stronger oracle.

If oracle exploits are mitigated, bots focusing there will have less to do (or will need to become honest arbitrageurs that arbitrage between DEX price and true price, which is fine).

  • Ethical Guidelines and Governance: This is a softer approach, but communities can set norms. For example, mining pools in the past have voluntarily refrained from certain attacks (like most Bitcoin miners won’t reorg even if profitable, due to norm). Ethereum’s validators could similarly agree not to include obviously malicious transactions if detected, though this is hard to coordinate and verify. Still, something like the Solana Foundation’s stance could become more common: where the community signals that if you engage in predatory MEV, you will be ostracized or your rewards reduced. Decentralized Autonomous Organizations (DAOs) governing protocols might enforce rules: e.g., a DAO could decide to compensate users who were victims of an exploit (essentially nullifying the bot’s gain) or develop user-protection features and fund them.
  • User Empowerment Tools: Until the systems are foolproof, giving users tools is vital. This includes:
    • MEV-aware wallets that route transactions through private relays (like Flashbots Protect RPC, or Blocknative’s service).
    • Interface warnings: If a user is about to do a trade with a very high slippage tolerance or on a very illiquid token (ripe for sandwich), the UI could warn “You are at high risk of a MEV attack on this trade. Proceed with caution or consider splitting the order.”
    • Educational resources integrated into platforms, so users coming to DeFi are not naive about these issues.

Considering morality, one could also ask: can we encode ethical behavior into the bots themselves? For instance, could there be a bot that intentionally only does “good MEV” (arbitrage, liquidations) and avoids attacking users? Technically yes, anyone could configure their bot as such. But economically, a bot that leaves money on the table (by not doing sandwiches that are profitable) will simply lose out to another bot that doesn’t share those scruples. In an open competition, pure altruism is unstable unless enforced or incentivized externally. This means that to truly have ethical autonomous agents, the environment must change such that unethical actions are either not profitable or not possible. This is a key insight: the ethics of bots are largely a function of the systems they operate in. Bots themselves have no conscience; they follow code and incentives. Thus the onus is on protocol designers and communities to craft incentive structures where profit aligns with social good (or at least, doesn’t come from social harm). It’s a classic mechanism design problem.

One promising framework is to treat the blockchain like a mini economy and apply mechanism design and game theory to steer it. Concepts like “credence goods” or harberger taxes on MEV have been floated. For example, some propose that if you profit hugely from MEV, maybe you pay a tax back into a fund that benefits users. How to implement that trustlessly is tricky, but maybe builders could opt into such schemes for goodwill.

Another angle is regulatory oversight in a decentralized context: if an on-chain action is deemed market manipulation, could an oracle or system flag it and prevent it? This drifts into centralized territory if not careful. Perhaps a decentralized court (like Kleros or similar) could adjudicate disputes or claims of malicious behavior, but that seems far off and hard to automate.

The bottom line of this discussion is that aligning autonomous agents with ethical outcomes requires multi-pronged effort: technical safeguards, incentive realignment, and perhaps a dose of governance. It is not solely a technical problem nor solely a moral one, but a socio-technical challenge. The good news is that many of the brightest minds in the blockchain space are actively working on MEV mitigation and oracle security, effectively pushing the frontier of what a fair and transparent market can be. The year 2025 finds us in a much more aware state than the DeFi summer of 2020 where these issues first exploded. Initiatives like Flashbots, Chainlink’s DECO/FSS, and various layer-1 protocol upgrades show that solutions are within reach.

Conclusion

Autonomous agents in blockchain systems, epitomized by MEV bots and oracle-exploiting bots, force us to confront difficult ethical questions at the intersection of technology, economics, and morality. These bots are a natural outgrowth of the capabilities and incentives present in decentralized networks: given transparency, permissionless access, and financial opportunity, it was inevitable that algorithms would rise to capitalize on every edge. In doing so, they have produced outcomes both beneficial — tighter markets, rapid liquidation of risky positions, innovative forms of on-chain liquidity — and detrimental — unfair advantages, wealth extraction from unsuspecting users, network congestion, and potential destabilization of consensus.

Through our analysis, we have seen that context matters. Not all bot activity is equal, and not all blockchains leave the same gaps for exploitation. This suggests that with careful design, it’s possible to enjoy the benefits of autonomous agents while curbing their harms. The ethical design of decentralized systems is an evolving art. Concepts like “toxic vs non-toxic MEV” now frame community discussions, indicating that the ecosystem itself is drawing moral lines and striving to enforce them via technology. It is heartening that solutions are being actively implemented: Ethereum’s move toward proposer-builder separation and private mempool channels, Solana’s crack-down on malicious behavior and exploration of fair scheduling, and application-layer innovations like batch auctions all point toward a future where being a user of DeFi doesn’t feel like venturing into a hostile jungle.

Ultimately, the ethics of autonomous agents boil down to a question: can we create a decentralized financial system that retains its open, permissionless nature without subjecting participants to exploitation by faster or more privileged actors? The work is ongoing. It may never be perfect — as in any market, there will always be those with an edge. But the degree matters. If we can reduce the invisible taxes and ensure that any value extracted by bots is at least coupled with value provided, we will have achieved a more just equilibrium.

One possible vision is a blockchain ecosystem where the role of arbitrageurs and liquidators is formalized and competitive in a healthy way (providing services for a modest reward), while opportunities to exploit others’ ignorance or timing are largely closed off. Oracles in that vision would be robust, perhaps using decentralized governance or economic guarantees to prevent single points of failure. Users could transact with confidence, knowing the protocol has their back regarding execution fairness. Bots might still exist, but they’d be bots-as-servants of the system’s efficiency, not bots-as-predators hunting users.

Reaching this vision will require continued collaboration between researchers, protocol developers, and the community. Every protocol upgrade or new design should be evaluated not just for throughput or features, but also for how it reallocates power between users, agents, and validators. Ethical considerations should become a standard part of blockchain improvement proposals.

In conclusion, autonomous agents in blockchain systems test our ability to imbue technology with our collective values. They reflect the system we create. If unchecked, they reflect the raw law of the jungle — might (or speed) makes right. If thoughtfully constrained, they can reflect a more civilized ethos — one where innovation thrives but not at the undue expense of the vulnerable. Blockchain governance, whether on-chain or off-chain, is the vehicle through which we must assert these values. The coming years will determine how successfully we can tame the darker aspects of MEV and oracle exploitation. The stakes are high: the credibility and inclusivity of decentralized finance depend on it. By addressing these challenges head-on, we move closer to a world where autonomous code and human ethics are not at odds, but in harmony, driving forward a new era of fair and open financial systems for all.

References

Leave a Reply

Your email address will not be published. Required fields are marked *