Related papers: Benchmarking LLM-Based Static Analysis for Secure …

Smart contracts are programs that execute transactions involving independent parties and cryptocurrencies. As programs, smart contracts are susceptible to a wide range of errors and vulnerabilities. Such vulnerabilities can result in…

Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility. Black-box models obscure their reasoning, while recent…

Automatic software verifiers have become increasingly effective at the task of checking software against (formal) specifications. Yet, their adoption in practice has been hampered by the lack of such specifications in real world code. Large…

We present a toolchain for developing and verifying smart contracts that can be executed on Bitcoin. The toolchain is based on BitML, a recent domain-specific language for smart contracts with a computationally sound embedding into Bitcoin.…

A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing (SAST) tools. Numerous false positives (FPs) in these reports reduce the effectiveness of…

The prevalence and strong capability of large language models (LLMs) present significant safety and ethical risks if exploited by malicious users. To prevent the potentially deceptive usage of LLMs, recent works have proposed algorithms to…

Prompt injection attacks exploit vulnerabilities in large language models (LLMs) to manipulate the model into unintended actions or generate malicious content. As LLM integrated applications gain wider adoption, they face growing…

Smart contracts are programs stored on a blockchain that run when predetermined conditions are met. However, designing and implementing a smart contract is not trivial since upon deployment on a blockchain, it is no longer possible to…

The adoption of blockchain based distributed ledgers is growing fast due to their ability to provide reliability, integrity, and auditability without trusted entities. One of the key capabilities of these emerging platforms is the ability…

With the rapid advancement of blockchain technology, smart contracts have enabled the implementation of increasingly complex functionalities. However, ensuring the security of smart contracts remains a persistent challenge across the stages…

When deploying large language models (LLMs), it is important to ensure that these models are not only capable, but also reliable. Many benchmarks have been created to track LLMs' growing capabilities, however there has been no similar focus…

In recent years, artificial intelligence (AI) and machine learning (ML) are reshaping society's production methods and productivity, and also changing the paradigm of scientific research. Among them, the AI language model represented by…

Large language models (LLMs) often present answers with high apparent confidence despite lacking an explicit mechanism for reasoning about certainty or truth. While existing benchmarks primarily evaluate single-turn accuracy, truthfulness…

There is a growing need for hardware-software contracts which precisely define the implications of microarchitecture on software security-i.e., security contracts. It is our view that such contracts should explicitly account for…

While a plethora of machine learning (ML) models are currently available, along with their implementation on disparate platforms, there is hardly any verifiable ML code which can be executed on public blockchains. We propose a novel…

Large language models (LLMs) are increasingly deployed in settings where reasoning, such as multi-step problem solving and chain-of-thought, is essential. Yet, current evaluation practices overwhelmingly report single-run accuracy while…

We present REMARK-LLM, a novel efficient, and robust watermarking framework designed for texts generated by large language models (LLMs). Synthesizing human-like content using LLMs necessitates vast computational resources and extensive…

Large Language Models (LLMs) effectiveness is usually evaluated by means of benchmarks such as MMLU, ARC-C, or HellaSwag, where questions are presented in their original wording, thus in a fixed, standardized format. However, real-world…

Large Language Models (LLMs) are increasingly applied to automated software testing, yet their ability to generalize beyond memorized patterns and reason about natural language bug reports remains unclear. We present a systematic evaluation…

Blockchain platforms and smart contracts are vulnerable to security breaches. Security breaches of smart contracts have led to huge financial losses in terms of cryptocurrencies and tokens. In this paper, we present a systematic survey of…