The alignment between RNA sequences and structures in foundation models (FMs) has yet to be thoroughly investigated. Existing FMs have struggled to establish sequence-structure alignment, hindering the seamless flow of genomic information between RNA sequences and structures. In this study, we introduce OmniGenome, an RNA FM trained to align RNA sequences with respect to secondary structures through structure-contextualized modelling. This alignment enables free and bidirectional mappings between sequences and structures by utilizing a flexible RNA modelling paradigm that supports versatile input and output modalities, i.e., sequence and/or structure as input/output. We implement RNA design and zero-shot secondary structure prediction as case studies to evaluate the Seq2Str and Str2Seq mapping capabilities of OmniGenome. Results on the EternaV2 benchmark show that OmniGenome solved 74% of puzzles, whereas existing FMs solved only up to 3% of the puzzles due to the lack of sequence-structure alignment. We leverage four comprehensive in-silico genome modelling benchmarks to evaluate performance across a diverse set of downstream genome tasks, where the results show that OmniGenome achieves state-of-the-art performance on RNA and DNA benchmarks, even without any training on DNA genomes.

Web application firewall (WAF) plays an integral role nowadays to protect web applications from various malicious injection attacks such as SQL injection, XML injection, and PHP injection, to name a few. However, given the evolving sophistication of injection attacks and the increasing complexity of tuning a WAF, it is challenging to ensure that the WAF is free of injection vulnerabilities such that it will block all malicious injection attacks without wrongly affecting the legitimate message. Automatically testing the WAF is, therefore, a timely and essential task. In this paper, we propose DaNuoYi, an automatic injection testing tool that simultaneously generates test inputs for multiple types of injection attacks on a WAF. Our basic idea derives from the cross-lingual translation in the natural language processing domain. In particular, test inputs for different types of injection attacks are syntactically different but may be semantically similar. Sharing semantic knowledge across multiple programming languages can thus stimulate the generation of more sophisticated test inputs and discovering injection vulnerabilities of the WAF that are otherwise difficult to find. To this end, in DaNuoYi, we train several injection translation models by using multi-task learning that translates the test inputs between any pair of injection attacks. The model is then used by a novel multi-task evolutionary algorithm to co-evolve test inputs for different types of injection attacks facilitated by a shared mating pool and domain-specific mutation operators at each generation. We conduct experiments on three real-world open-source WAFs and six types of injection attacks, the results reveal that DaNuoYigenerates up to 3:8× and 5:78× more valid test inputs (i.e., bypassing the underlying WAF) than its state-of-the-art single-task counterparts and the context-free grammar-based injection construction.

Recent studies have revealed the vulnerability of pre-trained language models to adversarial attacks.Adversarial defense techniques have been proposed to reconstruct adversarial examples within feature or text spaces.However, these methods struggle to effectively repair the semantics in adversarial examples, resulting in unsatisfactory defense performance.To repair the semantics in adversarial examples, we introduce a novel approach named Reactive Perturbation Defocusing (RAPID), which employs an adversarial detector to identify the fake labels of adversarial examples and leverages adversarial attackers to repair the semantics in adversarial examples.Our extensive experimental results, conducted on four public datasets, demonstrate the consistent effectiveness of RAPID in various adversarial attack scenarios.For easy evaluation, we provide a click-to-run demo of RAPID at https://tinyurl.com/22ercuf8.

The advancement of aspect-based sentiment analysis (ABSA) has highlighted the lack of a user-friendly framework that can significantly reduce the difficulty of reproducing state-of-the-art ABSA performance, especially for beginners. To meet this demand, we present PyABSA, a modularized framework built on PyTorch for reproducible ABSA. To facilitate ABSA research, PyABSA supports several ABSA subtasks, including aspect term extraction, aspect sentiment classification, and end-to-end aspect-based sentiment analysis. With just a few lines of code, the result of a model on a specific dataset can be reproduced. With a modularized design, PyABSA can also be flexibly extended to incorporate new models, datasets, and other related tasks. Additionally, PyABSA highlights its data augmentation and annotation features, which significantly address data scarcity. The project is available at: https://github.com/yangheng95/PyABSA.