The rapid growth of decentralized applications, while revolutionizing financial transactions, has created an attractive target for malicious attacks.Existing approaches to detecting attacks often rely on predefined rules or simplistic and overly-specialized models, which lack the flexibility to handle the wide spectrum of diverse and dynamically changing attack types.To address this challenge, we present a general and extensible framework, MoE (Monitoring Ethereum), that leverages runtime verification to detect a wide range of attacks on Ethereum.MoE features an expressive attack modeling language, based on Metric First-order Temporal Logic (MFOTL), that can formalize a wide range of attacks.We integrate a novel semantic lifting approach that extracts system behaviors relevant for various attacks, which can be analyzed using the monitoring tool MonPoly.Furthermore, we also equip MoE with quantitative capabilities to evaluate the similarity between a transaction and an attack formula to enhance its performance in identifying attacks, including near-miss attacks.We carry out extensive experiments with MoE on a labeled benchmark and a large-scale dataset containing over one million transactions.On the labeled benchmark, MoE successfully detects 92.0% attacks and achieves a 45.0% higher recall rate than competing state-of-the-art tool.MoE finds 3,319 attacks with 95.4% precision on the large dataset.Furthermore, MoE uses quantitative analysis to uncover 8% additional attacks.Finally, the average time for * Xinyao Xu and Ziyu Mao contributed equally.

Graph databases (GDBs) are increasingly used in many applications. However, their advanced features make them prone to logic bugs. Despite advances in GDB testing, a common limitation of current approaches is the lack of ground truth for their test oracles. This results in both incorrectly identified bugs and overlooked bugs. We introduce GQS (Graph Query Synthesis), the first automated testing approach for detecting logic bugs in graph databases (GDBs) based on an established ground truth. GQS starts by randomly generating a graph and selecting a set of properties associated with its elements, whose key-value pairs form the expected result set serving as the ground truth. It then synthesizes a query that should retrieve these values from the graph. When the query is executed on the graph by the GDB under test, any discrepancy between the actual result set and the ground truth indicates a logic bug. To extensively test a GDB, we develop novel techniques that synthesize both syntactically and semantically complex queries. We implement GQS in a tool that incorporates the first Cypher query synthesizer specifically designed for testing GDBs. Our tool finds 36 previously unknown bugs across four production GDBs, of which 26 are logic bugs, with some remaining undetected for up to five years. Additionally, our tool demonstrates superior effectiveness in bug detection compared to the state-of-the-art testers.

Isolation bugs, stemming especially from design-level defects, have been repeatedly found in carefully designed and extensively tested production databases over decades. In parallel, various frameworks for modeling database transactions and reasoning about their isolation guarantees have been developed. What is missing however is a mathematically rigorous and systematic framework with tool support for formally verifying a wide range of such guarantees for all possible system behaviors. We present the first such framework, VerIso, developed within the theorem prover Isabelle/HOL. To showcase its use in verification, we model the strict two-phase locking concurrency control protocol and verify that it provides strict serializability isolation guarantee. Moreover, we show how VerIso helps identify isolation bugs during protocol design. We derive new counterexamples for the TAPIR protocol from failed attempts to prove its claimed strict serializability. In particular, we show that it violates a much weaker isolation level, namely, atomic visibility.

Investigating how websites use sensitive user data is an active research area. However, research based on automated measurements has been limited to those websites that do not require user authentication. To overcome this limitation, we developed a crawler that automates website registrations and newsletter subscriptions and detects both security and privacy threats at scale.

Substantial research efforts have been devoted to studying the performance optimality problem for distributed database transactions. However, they focus just on optimizing transactional reads, and thus overlook crucial factors, such as the efficiency of writes, which also impact the overall system performance. Motivated by a recent study on Twitter's workloads showing the prominence of write-heavy workloads in practice, we make a substantial step towards performance-optimal distributed transactions by also aiming to optimize writes, a fundamentally new dimension to this problem. We propose a new design objective and establish impossibility results with respect to the achievable isolation levels. Guided by these results, we present two new transaction algorithms with different isolation guarantees that fulfill this design objective. Our evaluation demonstrates that these algorithms outperform the state of the art.

Transactional isolation is critical to the functional correctness of database management systems (DBMSs). Much effort has recently been devoted to finding isolation bugs and validating isolation fulfilment in production DBMSs. However, there are still challenges that existing isolation checkers have not yet fully addressed. For instance, they may overlook bugs, incur high checking overhead, and return hard-to-understand counterexamples. We present IsoVista, the first black-box isolation checking system that encompasses all the following features. It builds on faithful characterizations of a range of isolation levels, ensuring the absence of both false positives and missed bugs in collected DBMS execution histories. IsoVista exhibits superior checking efficiency, compared to the state-of-the-art, and visualizes violation scenarios, facilitating the understanding of bugs found. It also supports profiling and benchmarking the performance of isolation checkers under various workloads, assisting developers of both DBMSs and checkers. We showcase all these features through user-friendly interfaces.

Snapshot isolation (SI) is a prevalent weak isolation level that avoids the performance penalty imposed by serializability and simultaneously prevents various undesired data anomalies. Nevertheless, SI anomalies have recently been found in production cloud databases that claim to provide the SI guarantee. Given the complex and often unavailable internals of such databases, a black-box SI checker is highly desirable. In this paper we present PolySI, a black-box checker that efficiently checks SI and provides understandable counterexamples upon detecting violations. PolySI builds on a characterization of SI using generalized polygraphs (GPs), for which we establish its soundness and completeness. PolySI employs an SMT solver and also accelerates SMT solving by utilizing a compact constraint encoding of GPs and domain-specific optimizations for pruning constraints. As our extensive assessment demonstrates, PolySI successfully reproduces all of 2477 known SI anomalies, detects novel SI violations in three production cloud databases, identifies their causes, outperforms the state-of-the-art black-box checkers under a wide range of workloads, and can scale up to large workloads.

The relational calculus (RC) is a concise, declarative query language. However, existing RC query evaluation approaches are inefficient and often deviate from established algorithms based on finite tables used in database management systems. We devise a new translation of an arbitrary RC query into two safe-range queries, for which the finiteness of the query’s evaluation result is guaranteed. Assuming an infinite domain, the two queries have the following meaning: The first is closed and characterizes the original query’s relative safety, i.e., whether given a fixed database, the original query evaluates to a finite relation. The second safe-range query is equivalent to the original query, if the latter is relatively safe. We compose our translation with other, more standard ones to ultimately obtain two SQL queries. This allows us to use standard database management systems to evaluate arbitrary RC queries. We show that our translation improves the time complexity over existing approaches, which we also empirically confirm in both realistic and synthetic experiments.

Security-Aware Query Processing is the problem of computing answers to queries in the presence of access control policies. We present general impossibility results for the existence of optimal algorithms for Security-Aware Query Processing and classify query languages for which such algorithms exist. In particular, we show that for the relational calculus there are no optimal algorithms, whereas optimal algorithms exist for some of its fragments, such as the existential fragment. We also establish relationships between two different models of Fine-Grained Access Control, called Truman and Non-Truman models, which have been previously presented in the literature as distinct. For optimal Security-Aware Query Processing, we show that the Non-Truman model is a special case of the Truman model for boolean queries in the relational calculus, moreover the two models coincide for more powerful languages, such as the relational calculus with aggregation operators. In contrast, these two models are distinct for non-boolean queries.