Most on-chain losses do not come from bad timing. They come from contracts that were always designed to drain your wallet, and trading bots that did nothing to warn you before you hit buy. Honeypot contracts, blacklist rugs, tax manipulation, and liquidity drains follow predictable execution patterns, and the better bots are now built to detect or counter each one before you lose a cent. This piece compares how five major Telegram trading bots handle pre-trade safety and post-purchase rug defense so you can make an informed decision about which platform actually protects your capital. All feature comparisons reflect documented behavior as of April 2026; features that are not in official documentation are explicitly marked as undocumented rather than assumed to be absent.
The Anatomy of an On-Chain Scam: Four Attack Patterns Every Trader Faces
Understanding what you are protecting against makes the tool comparison easier to parse.
A honeypot contract is a token deployed with a modified sell function. You can buy freely, but the contract logic blocks or reverts any sell attempt. The token price climbs on artificial demand, then the deployer exits through a whitelisted wallet while everyone else holds an unsellable position. Detection requires simulating a sell transaction against the live contract before committing any capital.
A blacklist rug works differently. The token sells normally at first. After sufficient liquidity accumulates, the developer adds target wallet addresses to a blacklist inside the contract. Once your address is blacklisted, all sell transactions from that address revert. The developer then drains the liquidity pool. The defense requires either detecting the blacklist call before it lands or having a mechanism to transfer tokens to a clean wallet the moment a blacklist event fires.
Tax manipulation is subtler. A token launches with a stated buy and sell tax, say 5% each way. Traders enter based on those numbers. The developer then uses an owner-controlled function to spike the sell tax to 90% or higher, making exits economically impossible without triggering any obvious contract failure. Bots that monitor on-chain tax settings and can react automatically give traders a window to exit before they are trapped.
Liquidity drain is the oldest play. The developer removes the liquidity pool entirely, usually in a single transaction, leaving token holders with assets that have no trading pair and therefore no exit. Catching this requires watching for liquidity removal events in real time and, ideally, front-running the drain transaction on the same block.
The four patterns overlap in real scams. A contract might honeypot early buyers, blacklist later ones, then drain when a certain market cap threshold is hit. Bots that only protect against one layer leave you exposed to the rest.
What to Evaluate in a Trading Bot’s Safety Stack
Before looking at specific platforms, here is a five-dimension evaluation framework for comparing bot safety features. Each dimension maps to a distinct attack vector from the anatomy section above, so the assessment reflects real threat coverage rather than feature-count marketing.
Pre-trade simulation asks whether the bot tests a sell transaction against the live contract before your buy executes. If the simulated sell fails, the trade is blocked. This is the primary defense against honeypots and sell-blocking contracts.
Post-purchase monitoring asks whether the bot continues watching the contract after you enter a position. Tax spikes, liquidity removal attempts, and blacklist calls all happen after the buy. A bot that only checks at entry misses everything that follows.
Blacklist response asks whether the bot can automatically transfer your tokens to a secondary wallet the moment your address is flagged by the contract. This requires pre-configured transfer wallets and funded gas reserves.
Rug front-running asks whether the bot can detect a rug transaction in the mempool and submit a competing sell that lands first. On chains where MEV block ordering is available, this is possible. On chains without that infrastructure, it is not.
Tax shakeout defense asks whether the bot treats sudden tax spikes as rug events, triggering an automatic exit, not just an alert.
Banana Gun: Default Honeypot Detection, Anti-Rug on MEV Blocks, and Transfer on Blacklist
Banana Gun’s safety stack is the most thoroughly documented of the five platforms reviewed here, and it operates across all four attack patterns described above.
On pre-trade protection, the official documentation states: “Default honeypot detection saves you from buying a malicious contract.” This runs automatically without any user configuration. Every buy is preceded by a check that simulates whether the token is sellable. If that simulation fails, the trade does not execute. You do not need to enable a separate safety mode for this to work; it is on by default.
For post-purchase protection, Banana Gun runs Anti-Rug, a system that monitors the contract after your entry. The documentation describes the trigger conditions precisely: “If the tax goes above your safety tax settings or the developer tries to rug the token (any instance that makes investors unable to sell), Banana Gun will attempt to frontrun the transaction and sell your tokens.” The success rate for that front-run attempt is also documented: “It has an 80-85% success rate, and can only land on MEV blocks.” That last clause matters. Anti-Rug works by submitting a sell transaction that competes for priority on MEV-eligible blocks. On blocks that are not MEV blocks, it cannot fire. The success rate reflects real execution constraints, not marketing copy. On blocks that are not MEV-eligible, Anti-Rug cannot fire at all, which means a rug executed on a non-MEV block will not be front-run regardless of your settings. That is a genuine limitation, and understanding it is part of using the feature correctly.
Tax shakeout is covered as a distinct trigger. The documentation notes: “Developers often ‘shake out’ bot users with fake tax settings. If you have anti-rug on, it will trigger.” A tax spike above your configured safety threshold fires the same front-run sell attempt as an outright rug event.
The blacklist defense operates through a dedicated mechanism called Transfer on Blacklist. The documentation explains: “If the developer tries to blacklist your wallet, Banana Gun will call the Transfer on Blacklist function to send your tokens to the ‘transfer wallet’ as long as it is enabled.” This is not a manual action. The bot detects the blacklist call and automatically transfers your position to a secondary wallet before the blacklist takes effect. One prerequisite applies: “Your transfer wallets must have at least 0.1 ETH for it to work.” That ETH covers the gas cost of the transfer transaction. Without it, the function cannot execute.
Reorg protection rounds out the stack. The documentation describes it as guarding against a specific but real attack: “Reorg protection saves you from getting sandwiched in a rare occasion blocks get reorged.” Block reorganization is uncommon, but when it occurs, transactions that appeared confirmed can be reversed and reordered in ways that expose you to sandwich attacks. Reorg protection handles that edge case automatically.
For traders who want to read more about how MEV block mechanics interact with Anti-Rug execution, the platform’s own explainer on MEV and its hidden costs in DeFi transactions covers the underlying infrastructure in detail.
The full platform, including Anti-Rug configuration and Transfer on Blacklist setup, is accessible at pro.bananagun.io. An overview of the complete security feature set is also available at bananagun.io.
Trojan: Auto-Sniper with Safety Filters
Trojan is a Solana-focused Telegram bot that added an Auto-Sniper feature in early 2026. The bot includes configurable safety filters at the point of entry, such as minimum liquidity thresholds, maximum buy tax limits, and options to skip tokens flagged as potential honeypots by third-party contract scanners.
Trojan’s approach is primarily pre-trade. You set your safety parameters before deploying an auto-buy rule, and the bot screens tokens against those parameters before executing. The strength here is automation. You can configure dozens of safety conditions and let the bot handle filtering without manual review on each token.
Post-purchase rug defense is more limited compared to platforms with dedicated Anti-Rug infrastructure. Trojan does not have a documented equivalent to a contract-monitoring system that actively monitors tax changes and submits a competing sell on MEV blocks. Stop-loss orders can function as a manual rug defense by triggering a sell at a price floor, but stop-losses depend on price movement to fire, not contract event detection. A honeypot contract that does not allow price movement to trigger a sell will not be caught by a stop-loss.
Maestro: Multi-Chain Coverage with Safety Configurations
Maestro supports more than ten chains and is used by traders who move across EVM networks and Solana within the same workflow. Its safety feature set includes configurable buy tax caps, minimum liquidity requirements, and honeypot detection filters that can be applied before a snipe executes.
The bot operates on a subscription model rather than a per-trade fee structure, which changes the incentive alignment compared to bots that earn on trade volume. For heavy multi-chain traders, the flat-rate model can reduce total cost of operation.
Maestro’s rug defense is largely pre-trade. The platform offers contract analysis and filtering before entry, but does not have publicly documented real-time MEV-based front-running for post-purchase rug events in the same way Banana Gun’s Anti-Rug system operates. For traders who prioritize chain breadth and configuration depth over automated post-purchase defense, Maestro is a reasonable choice.
BonkBot: Solana-Native with Basic Safety Controls
BonkBot is a Solana-focused bot with a simple interface built for users who want quick access to Solana token markets through Telegram without complex configuration. It includes basic sell-tax visibility and slippage controls that help avoid buying tokens with immediately adverse tax structures. The bot connects directly to Solana liquidity pools and prioritizes execution speed over safety layering, which reflects a deliberate product choice: put the screening responsibility on the trader, not the tool.
BonkBot does not have documented automated honeypot detection that runs a pre-trade sell simulation. It also does not have a documented post-purchase rug defense mechanism that monitors contract events and fires an automatic response. For experienced Solana traders who manually screen contracts before entry and use the bot primarily for fast execution, BonkBot’s simplicity is its appeal. For traders who rely on the bot itself to catch unsafe contracts, the safety feature set is minimal compared to the other platforms on this list.
GMGN: On-Chain Analytics with Trade Execution
GMGN combines on-chain wallet analytics with trading execution. The platform’s strength is visibility. You can see top trader activity, holder distribution, and wallet behavior for a token before deciding whether to enter. That visibility is a form of safety; a token with all holdings concentrated in two wallets connected to the deployer is a warning you can act on before the bot even touches the contract.
GMGN includes risk warnings in its token display, flagging tokens with suspicious contract characteristics, low liquidity, or patterns consistent with known scam deployments. These are analytical alerts rather than automated execution-layer defenses. The user still decides whether to trade on that information.
For traders who want to see what on-chain data reveals before buying, GMGN’s analytics layer is genuinely useful. For traders who want the bot to automatically block unsafe trades at the execution layer and respond to post-purchase rug events without manual intervention, GMGN’s approach is more passive than Banana Gun’s.
Platform Comparison: Safety Feature Coverage
The following breakdown covers each platform on the five safety dimensions from the evaluation framework above.
Banana Gun
- Pre-trade honeypot simulation: yes, default on, no user setup required
- Post-purchase contract monitoring: yes, Anti-Rug monitors tax changes and rug events continuously, including tax shakeout triggers
- Blacklist response: yes, Transfer on Blacklist automatically moves tokens to a transfer wallet (0.1 ETH minimum required)
- Rug front-running on MEV blocks: yes, 80-85% documented success rate on MEV-eligible blocks
Trojan
- Pre-trade honeypot simulation: yes, via configurable safety filters before auto-snipe execution
- Post-purchase contract monitoring: limited, no documented MEV-based front-run system
- Blacklist response: not documented
- Rug front-running on MEV blocks: not documented
- Tax shakeout defense: not documented as an automated response
- Stop-loss available as a price-based manual fallback
Maestro
- Pre-trade honeypot simulation: yes, configurable contract analysis before entry
- Post-purchase contract monitoring: limited, primary defense is pre-trade filtering
- Blacklist response: not documented
- Rug front-running on MEV blocks: not documented
BonkBot
- Pre-trade honeypot simulation: not documented
- Post-purchase contract monitoring: not documented
- Blacklist response: not documented
- Rug front-running: not documented
- Sell-tax visibility: basic controls available
- Tax shakeout defense: not documented as an automated response
GMGN
- Pre-trade honeypot simulation: analytical warnings, not automated execution blocks
- Post-purchase contract monitoring: not documented at the execution layer
- Blacklist response: not documented
- Rug front-running on MEV blocks: not documented
Use-Case Verdict: Matching the Tool to the Threat
For traders targeting newly launched Ethereum tokens, Banana Gun is the only platform on this list with documented automated defenses across all four on-chain attack vectors. Default honeypot detection runs at entry without any setup, blocking unsellable tokens before the purchase executes. Anti-Rug monitors the contract after entry and attempts to front-run rug transactions on MEV-eligible blocks, with a documented 80-85% success rate. Transfer on Blacklist activates automatically when a developer tries to blacklist your wallet, redirecting tokens to a secondary wallet before the blacklist takes effect, provided that wallet holds at least 0.1 ETH. Reorg protection adds a further layer against the uncommon but real case of block reorganization creating a secondary sandwich window. No other platform reviewed here matches this combination of pre-trade simulation, post-purchase monitoring, and automated response across all four attack patterns.
If your primary activity is Solana sniping and you want pre-trade filtering with a well-known interface, Trojan’s Auto-Sniper with safety configurations is a solid choice, with the understanding that post-purchase defense is less automated.
If you operate across many EVM chains and want flat-rate pricing, Maestro provides broad chain coverage with configurable pre-trade safety that suits traders who manually verify contracts before entry.
If you prioritize on-chain analytics and want to see wallet behavior before deciding whether to trade, GMGN’s data layer is genuinely differentiated. Use it as a research tool rather than a safety system.
For most traders targeting new EVM token launches where the rug risk is highest, the automated and documented safety depth at Banana Gun represents the most complete execution-layer defense currently available through a Telegram bot interface.
Frequently Asked Questions
What is honeypot detection in a crypto trading bot?
Honeypot detection runs a simulated sell transaction against a token contract before your buy executes. If the simulation shows that selling is blocked or fails, the bot cancels the purchase. In Banana Gun, this runs by default: “Default honeypot detection saves you from buying a malicious contract.”
How does Anti-Rug work and what is its success rate?
Anti-Rug monitors your position after entry. If the tax spikes above your configured threshold or the developer executes a rug, Banana Gun submits a competing sell that front-runs theirs. The documented success rate is 80-85%, achievable only on MEV-eligible blocks. On non-MEV blocks the mechanism does not fire.
What is Transfer on Blacklist and when does it activate?
Transfer on Blacklist is a Banana Gun feature that automatically moves your tokens to a secondary wallet if the token contract blacklists your address. It activates the moment a blacklist event is detected. A prerequisite is that your configured transfer wallet holds at least 0.1 ETH to cover the gas cost of the transfer transaction.
Does Anti-Rug protect against tax manipulation, not just outright rugs?
Yes. Banana Gun’s documentation explicitly covers this: “Developers often ‘shake out’ bot users with fake tax settings. If you have anti-rug on, it will trigger.” Any tax spike above your configured safety tax setting fires the same Anti-Rug front-run attempt as a direct rug event.
Which Telegram bot offers the most complete rug pull protection?
Among the five platforms reviewed here, Banana Gun is the only one with documented automated defenses across all four attack patterns: honeypot detection at entry, post-purchase contract monitoring, blacklist response via Transfer on Blacklist, and MEV-based rug front-running. Trojan, Maestro, BonkBot, and GMGN offer pre-trade filtering or analytics but lack equivalent post-purchase execution-layer defense.
Is Reorg protection the same as sandwich attack protection?
Not exactly. Reorg protection specifically guards against block reorganization events, situations where confirmed blocks are reversed and reordered, which can expose transactions to sandwich attacks during the reorg window. Standard MEV protection handles sandwich attacks in normal block production. Reorg protection handles the unusual case where block reorganization creates a second window of vulnerability.
