{"id":"0721c3fe-18e5-47a5-a2c0-0f8ac6cdf668","arxiv_id":"2506.07974","paper_version":1,"verdict":"REJECT","confidence":"MODERATE","novelty_score":3.0,"correctness_risk":"high","formal_verification":"none","parameter_count":7,"one_line_summary":"A static analysis of nearly 50,000 Ethereum NFT contracts finds that 22.6% contain multiple rug-pull-related code patterns, though the study does not validate these flags against confirmed scams.","lead":"This preprint applies the Slither static analysis tool to 49,940 verified NFT contracts on Ethereum and assigns each a risk score based on patterns like selfdestruct, delegatecall, and owner-only minting. It reports that 22.6% of these contracts score high risk, but it does not check whether any of those contracts actually executed a rug pull.","discovery_kind":"new_application","skeptic_critique":{"model":"deepseek-v4-flash","headline":"The 22.6% 'high-risk backdoor' figure conflates common, often benign Solidity patterns with malicious intent; the paper's own Section VII-C admits static scoring cannot distinguish intentional logic, so the central conclusion is unsupported.","rationale":"The reader's weakest assumption — that hand-weighted static patterns are a valid measure of deliberate rug-pull risk — is exactly the load-bearing concern I identify. The paper's own limitation statement in Section VII-C directly undermines the central interpretation, and no ground-truth validation, baseline, or artifact release is provided to close the gap. Because the stress-test does not reveal a new objection beyond what the reader already identified, and because the reader's REJECT verdict already reflects the severity of this flaw, no verdict adjustment is needed. The concern is not that static analysis is useless; it is that the specific numeric claim ('over 22% of NFT contracts show high-risk patterns such as selfdestruct... recurring deployment strategies') is an interpretive overreach relative to what the methodology can establish. A labeled validation experiment would either rescue the claim by showing high precision on known malicious contracts or confirm that the score measures generic code features rather than hidden backdoors. Either outcome would settle the matter, which is why I recommend keeping the REJECT verdict while noting that the underlying data collection pipeline could be a useful contribution if re-scoped as 'prevalence of risky patterns' rather than 'hidden backdoors.'","tokens_in":11825,"tokens_out":2358,"duration_ms":35937,"concrete_test":"Build a labeled validation set of 200 contracts: 100 confirmed rug-pull contracts (from public incident lists such as Huang et al. [42] or blockchain scam registries) and 100 audited, known-benign NFT contracts (e.g., popular OpenZeppelin-based collections with public audits). Run the paper's exact Slither pipeline and Table I scoring on both sets, then compute the precision and recall of the high-risk tier (score >= 5). If benign contracts are frequently labeled high-risk or malicious contracts are frequently low-risk, the 22.6% prevalence cannot be interpreted as a backdoor detection rate. A complementary check: have two independent security auditors manually classify a random sample of 50 high-risk contracts as 'deliberate backdoor' vs. 'benign or negligent design'; if fewer than half are judged deliberate, the intentionality claim in Section VII-A fails.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The central claim depends on treating the Table I weighted score as a proxy for deliberate rug-pull design, but the scoring is neither validated nor calibrated. A contract reaches 'high risk' (score >= 5) with selfdestruct (+3), an external call in a loop (+2), and a deprecated compiler version (+1) — no owner-controlled withdrawal or mint needed. Conversely, standard OpenZeppelin ERC-721 contracts commonly inherit Ownable and expose owner-only mint/withdraw functions, which are counted as +2 each, so legitimate template usage can push contracts into the high-risk tier. Slither's 'external call in loop' finding also fires on batch-transfer loops that are routine in NFT airdrops. The paper itself states in Section VII-C that the risk scoring 'cannot differentiate between intentional malicious logic and negligibly insecure logic,' yet Section VII-A and the conclusion infer that the flagged patterns are 'unlikely to occur unintentionally' and 'strongly suggest' pre-engineered rug pulls. That inference is the load-bearing step: if high-risk contracts are mostly accidental, templated, or standard-featured, the 22.6% figure measures pattern prevalence, not hidden backdoors. The dataset also shows unexplained count inconsistencies (98,879 DISL contracts vs. ~98,000 NFT candidates vs. 49,940 final), and no code or data is released, but the dominant problem is the unsupported leap from static pattern co-occurrence to malicious intent.","agreement_with_reader":"agree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The paper presents a static analysis pipeline that filters verified Ethereum contracts from the DISL dataset to NFT-related contracts, runs Slither, assigns a weighted risk score based on six hand-picked vulnerability patterns, and classifies contracts into low/medium/high risk tiers. The headline result is that 22.6% of 49,940 analyzed contracts are high-risk, and the authors interpret this as evidence that rug-pull backdoors are deliberately pre-engineered rather than accidental. The paper also reports prevalence of individual patterns, co-occurrence statistics, example contracts, and mitigation recommendations.","tokens_in":12164,"tokens_out":4839,"duration_ms":56119,"significance":"If the central claim were established, a measurement showing that more than 20% of verified NFT contracts contain deliberate backdoor logic would be important for the smart-contract security community. The paper's pipeline design is straightforward, its use of a large public dataset (DISL) is appropriate, and the authors make a good-faith effort to describe preprocessing, static analysis, and scoring. However, the paper's own limitation statement in Section VII-C concedes that static scoring cannot distinguish intentional from negligent logic, which directly undercuts the headline interpretation. The heuristic weights in Table I are not validated against any ground-truth set of known rug pulls, and no baseline, sensitivity analysis, or uncertainty intervals are provided. The contribution is therefore best viewed as a descriptive prevalence study of certain code patterns, not as a detection study of hidden backdoors. With substantial reframing and additional validation, the dataset could be useful, but as written the load-bearing inference is unsupported.","major_comments":[{"comment":"Section VII-C states that the risk scoring 'cannot differentiate between intentional malicious logic and negligibly insecure logic,' yet Section VII-A (and the conclusion in Section VIII) asserts that contracts with these patterns are 'unlikely to occur by accident' and that the results 'strongly suggest' a recurring design model of deliberately retained control. This is the central inference of the paper, and it is contradicted by the paper's own limitation. Since Slither findings are syntactic pattern matches—for example, 'external call in a loop' fires on routine batch-transfer loops in OpenZeppelin-based contracts, and 'onlyOwner withdrawal' is standard in Ownable templates—the leap from pattern co-occurrence to malicious intent is unsupported. The authors must either add a validated ground-truth comparison (e.g., known rug-pull contracts versus known benign collections) or substantially weaken the claims to pattern prevalence.","section":"VII-C, VII-A, VIII"},{"comment":"Table I assigns weights of +3, +2, or +1 to six vulnerability patterns and sets a high-risk threshold of score ≥ 5, but no justification, validation, or sensitivity analysis is given. The weights and thresholds are free parameters; different choices would change the headline 22.6% figure. The paper does not compare the scoring against any labeled dataset of confirmed rug pulls, nor does it report precision, recall, or confidence intervals. Because the weights are arbitrary, the risk tiers are a definitional output of the authors' heuristic rather than a measured property. A concrete test would be to score a set of confirmed rug-pull contracts (e.g., from Huang et al. [42]) and a set of known benign collections and report separation; until then, the 22.6% figure is unvalidated.","section":"Table I, Section III-C"},{"comment":"Table II states that the DISL dataset contains 98,879 contracts, while Section III-A says DISL contains more than 514,000 contracts. Section IV-B says the NFT filter reduces 98,879 contracts to 'approximately 98,000 candidates,' which implies the NFT filter removed fewer than 1% of contracts, and then sanitization removes roughly half. These numbers do not reconcile: if DISL has 514k contracts, what is the 98,879? How can nearly all of 98,879 be NFT-relevant while the broader dataset is 514k? The manuscript should report the exact counts at each stage and explain the relationship between the DISL subset and the claimed 514k total.","section":"Section IV, Table II"},{"comment":"Section V-E states that 'over 22% of NFT contracts show high-risk patterns such as selfdestruct,' but selfdestruct appears in only 6,881 contracts (13.8% of the 49,940), and the high-risk tier (score ≥ 5) is a composite that can be reached without selfdestruct. Also, 'nearly 38% of contracts use onlyOwner in a way that grants unilateral financial control' is inferred from 18,925 contracts with an onlyOwner withdrawal/mint finding, but Slither's finding is a pattern match that does not establish unilateral control, since the function may be restricted by other conditions. The results section should report patterns as detected by the tool, not as confirmed privileges.","section":"Section V-E, Table III"},{"comment":"The paper describes a 'repeatable process' and a 'reproducible foundation,' but neither the filtering/sanitization scripts, the Slither wrapper, the risk-scoring implementation, nor the list of analyzed contract addresses is released. For a measurement study whose headline is a prevalence percentage, this makes independent verification impossible. At minimum, the authors should release the contract identifiers and the per-contract detection output.","section":"Section III and IV (reproducibility)"}],"minor_comments":[{"comment":"The Background section contains a duplicated paragraph: the second paragraph ('The NFT ecosystem is based on blockchain technologies...') repeats the content of the first paragraph nearly verbatim.","section":"Section II"},{"comment":"The index terms line has a typo: 'Index T erms' should be 'Index Terms.'","section":"Abstract/Index Terms"},{"comment":"Figure 2 is captioned 'MSmart analysis flow chart [55],' but the methodology uses Slither, and the caption/reference do not match the paper's pipeline.","section":"Figure 2"},{"comment":"References [54] and [56] are unrelated to NFT or smart-contract security (they concern LLM injection and genomic privacy); they appear to be padding and should be removed or replaced.","section":"References"},{"comment":"Table V lists anonymized contract IDs but no source addresses or verification links, so the 'representative examples' cannot be checked; the authors should provide real addresses or release the underlying data.","section":"Table V"}],"recommendation":"reject","confidential_remarks":"The manuscript reads like an extended abstract: it has duplicated paragraphs, unrelated references, and a central claim that overreaches the evidence. The main contribution would need to be reframed as a pattern-prevalence study and re-validated against ground-truth rug-pull contracts before it could meet the bar for publication. The count inconsistencies and lack of data release further weaken confidence in the reported numbers."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"The headline number — 22.6% of NFT contracts have hidden backdoors — is not supported by the paper's own evidence. What is actually new is a large-scale descriptive census: 49,940 verified NFT contracts run through Slither, with a sensible taxonomy of rug-pull-relevant patterns (selfdestruct, delegatecall, owner-only mint/withdraw, tx.origin checks). The pipeline is clearly described and the DISL source is public, so the raw prevalence statistics for individual patterns (e.g., 6,881 contracts with selfdestruct) are useful descriptive data.\n\nThe soft spots are substantial, and the stress-test note lands. The risk weights in Table I are arbitrary: no calibration, no ground truth, no baseline. The dataset counts are inconsistent (98,879 to ~98,000 to 49,940), and no code or data is released, so the whole thing is hard to verify. But the load-bearing problem is the interpretation. Section VII-C explicitly says the scoring 'cannot differentiate between intentional malicious logic and negligibly insecure logic,' yet Section VII-A and the conclusion claim these patterns are 'unlikely to occur unintentionally' and 'strongly suggest' pre-engineered rug pulls. That is a direct contradiction with the paper's own limitation statement. The stress-test examples are concrete: a standard OpenZeppelin ERC-721 contract with Ownable and an owner-only mint/withdraw can accumulate +4 without anything malicious, and Slither's 'external call in loop' fires on routine batch-transfer loops. So the 22.6% figure likely measures pattern prevalence, not backdoors.\n\nThe paper does cite the relevant prior work — Perez & Livshits, Pied-Piper — but it does not engage with their central lesson: vulnerable does not imply exploited, and pattern presence does not imply intent. That is exactly the mistake made here.\n\nWho is this for? Maybe a practitioner building a pre-screening triage tool, if the claims were reframed. As submitted, the central conclusion overreaches, and the absence of artifacts makes it hard to build on. Still, the dataset and pipeline are real, and the topic matters. I would send it to peer review rather than desk reject, because a serious referee could push the authors to either validate the scoring against a labeled corpus of actual rug pulls or soften the claims to pattern prevalence. As it stands, the 22.6% claim should not be repeated.","headline":"Real dataset, real pipeline, but the 22.6% 'hidden backdoor' figure is an unvalidated heuristic dressed as a finding.","tokens_in":12676,"tokens_out":2924,"would_cite":false,"duration_ms":36656,"reading_group":"maybe","serious_thinker":"no","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"Analyzing 49,940 verified NFT smart contracts with the Slither static analyzer, this paper claims that over 22% carry high-risk rug-pull indicators such as selfdestruct, suggesting hidden backdoors are systematically pre-engineered rather…","keywords":["NFT smart contracts","rug pull","static analysis","selfdestruct","delegatecall","risk scoring","Ethereum","hidden backdoors"],"falsifier":"Trace the on-chain history of a random sample of the 11,309 high-risk contracts: if they execute selfdestruct, drain funds, or freeze user assets at the same rate as unflagged contracts, the pattern score does not predict rug pulls. Alternatively, remove contracts that are near-direct copies of standard library templates and re-run the scoring; if the high-risk share collapses below the paper's 22.6%, the signal is template reuse rather than deliberate backdoor engineering.","tokens_in":11632,"feed_emoji":"🔍","tokens_out":5273,"duration_ms":54721,"temperature":0.7,"pith_summary":"This paper claims that hidden backdoors in NFT smart contracts are common enough to be a systemic pattern, not rare accidents. Running the Slither static analyzer over 49,940 verified NFT contracts on Ethereum and applying a weighted risk score, it finds that about 22.6% of contracts reach high-risk status through combinations of selfdestruct, delegatecall, owner-only mint/withdraw, tx.origin checks, and deprecated compiler versions. The intended upshot is that rug pulls can be detected and triaged at scale before deployment by flagging these code patterns. A sympathetic reader would take the paper's central claim to be that roughly one in four verified NFT contracts contains logic that can drain funds or seize control, and that this is frequently pre-engineered rather than accidental.","feed_headline":"One in four NFT contracts embeds high-risk rug-pull patterns","feed_subtitle":"Static scan of 49,940 verified Ethereum contracts finds selfdestruct and owner-only control coded in, not accidental.","key_machinery":"The machinery is a two-stage pipeline. First, Slither constructs a contract's control-flow graph, abstract syntax tree, and inheritance hierarchy to detect over 100 vulnerability patterns, with emphasis on selfdestruct, delegatecall, external calls in loops, owner-only or unrestricted mint/withdraw, and tx.origin-based access control. Second, a hand-assigned heuristic scoring model weights each finding (selfdestruct and delegatecall +3; external call in loop, unrestricted/owner-only withdraw/mint, and tx.origin checks +2; deprecated Solidity version +1) and classifies contracts with cumulative score ≥5 as high risk, 3–4 as medium, and 1–2 as low. The scoring model is what turns raw Slither findings into the paper's headline risk distribution.","core_discovery":"The central discovery is a measured prevalence: across 49,940 standalone, verified NFT contracts derived from the DISL dataset, 11,309 (22.6%) score at least 5 on the authors' heuristic risk scale and are labelled high-risk, 17,478 (35.0%) are medium, and 21,153 (42.4%) are low. Individual pattern counts reinforce the picture: 18,925 contracts use owner-only withdrawal, 14,478 have unrestricted mint access, 6,881 use selfdestruct, and 4,724 use delegatecall. The paper interprets the co-occurrence of these patterns—8,217 contracts with two or more high-severity indicators, 3,456 with three or more, and 62 with four or more—as evidence that many contracts are deliberately designed to retain deployer control after launch, supporting the hypothesis that rug pulls are pre-engineered through contract logic rather than opportunistic exits.","pith_inferences":["The paper does not link its flagged contracts to actual on-chain rug pull events; correlating the 11,309 high-risk contracts with transaction histories of fund drains or selfdestruct executions would test whether the score predicts real harm.","A substantial share of the high-risk signal may come from template reuse: many NFT collections start from OpenZeppelin base contracts with onlyOwner and mint logic, so a natural extension is to subtract template-derived patterns and re-measure the deliberate-backdoor rate.","Extending the same weighted scoring to other chains (Polygon, BNB Chain, Avalanche) would reveal whether the 22.6% prevalence is Ethereum-specific or a cross-ecosystem norm.","The paper's own admission that static analysis 'cannot differentiate between intentional malicious logic and negligibly insecure logic' suggests the strongest version of the claim—pre-engineering—will need dynamic confirmation, such as fuzzing or symbolic execution, to hold up."],"forward_implications":["If the 22.6% high-risk figure holds, NFT marketplaces can pre-screen collections by running static pattern scoring before listing, and label contracts with risk badges.","Security auditors gain a reproducible first-line filter that points human review to the small set of contracts with overlapping backdoor indicators.","Developers are given a concrete checklist: avoid selfdestruct and delegatecall, renounce ownership, restrict mint/withdraw conditions, and avoid tx.origin.","The co-occurrence data implies that exploitability escalates with pattern combinations, so a contract with selfdestruct plus delegatecall should be treated as more dangerous than one with either alone.","The near-38% rate of owner-only withdrawal suggests centralized post-deployment control, not decentralization, is the default for many NFT projects."],"supporting_citations":[{"why":"Supplies the Slither static analysis engine used to extract vulnerability patterns from each contract.","marker":"[8]"},{"why":"Provides the DISL repository of over 514,000 verified Ethereum contracts from which the NFT pool is filtered.","marker":"[21]"},{"why":"Establishes the behavioral taxonomy of fraudulent NFT creators and rug-pull patterns the scoring targets.","marker":"[3]"},{"why":"Measurement study of NFT rug pulls that supports the paper's interpretation of overlapping control patterns as pre-engineered.","marker":"[42]"},{"why":"Names OpenZeppelin ERC721/ERC1155 and Ownable libraries used to identify NFT-relevant contracts and to frame owner-only patterns.","marker":"[23]"},{"why":"Prior work revealing backdoor threats in Ethereum ERC token contracts that motivates the backdoor-specific taxonomy.","marker":"[5]"}],"fun_headline_variants":["Static scan finds 22.6% of NFT contracts at high rug-pull risk","NFT rug-pull backdoors lurk in nearly one in four verified contracts","22.6% of verified NFT contracts carry high-risk rug-pull patterns","Nearly one in four NFT smart contracts hides rug-pull backdoors","49,940 NFT contracts analyzed: 11,309 score high-risk for rug pulls"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The central interpretation rests on the assumption that co-occurrence of these weighted code patterns is a valid proxy for deliberate backdoor design; if selfdestruct, delegatecall, and owner-only controls are mostly accidental, inherited from standard templates, or common in benign NFT contracts, the 22.6% figure no longer measures rug-pull intent.","fun_headline_variants_meta":{"raw":{"variants":["Static scan finds 22.6% of NFT contracts at high rug-pull risk","NFT rug-pull backdoors lurk in nearly one in four verified contracts","22.6% of verified NFT contracts carry high-risk rug-pull patterns","Nearly one in four NFT smart contracts hides rug-pull backdoors","49,940 NFT contracts analyzed: 11,309 score high-risk for rug pulls"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.001168,"raw_usage":{"total_tokens":4868,"prompt_tokens":1019,"completion_tokens":3849,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":635,"completion_tokens_details":{"reasoning_tokens":3746}},"tokens_in":635,"tokens_out":3849,"duration_ms":29028,"temperature":1.0,"reasoning_tokens":3746,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-07T05:21:15.153972+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"Trace the on-chain history of a random sample of the 11,309 high-risk contracts: if they execute selfdestruct, drain funds, or freeze user assets at the same rate as unflagged contracts, the pattern score does not predict rug pulls. Alternatively, remove contracts that are near-direct copies of standard library templates and re-run the scoring; if the high-risk share collapses below the paper's 22.6%, the signal is template reuse rather than deliberate backdoor engineering.","supporting_citations":[{"cited_title":"Slither: A Static Analysis Framework for Smart Contracts","cited_arxiv_id":null,"evidence_quote":"Supplies the Slither static analysis engine used to extract vulnerability patterns from each contract."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Provides the DISL repository of over 514,000 verified Ethereum contracts from which the NFT pool is filtered."},{"cited_title":"Understanding Rug Pulls: An In-depth Behavioral Analysis of Fraudulent NFT Creators","cited_arxiv_id":null,"evidence_quote":"Establishes the behavioral taxonomy of fraudulent NFT creators and rug-pull patterns the scoring targets."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Measurement study of NFT rug pulls that supports the paper's interpretation of overlapping control patterns as pre-engineered."},{"cited_title":null,"cited_arxiv_id":null,"evidence_quote":"Names OpenZeppelin ERC721/ERC1155 and Ownable libraries used to identify NFT-relevant contracts and to frame owner-only patterns."},{"cited_title":"Pied-Piper: Revealing the Backdoor Threats in Ethereum ERC Token Contracts","cited_arxiv_id":null,"evidence_quote":"Prior work revealing backdoor threats in Ethereum ERC token contracts that motivates the backdoor-specific taxonomy."}],"review_version":1}