This is the week of Black Hat and Defcon, which means a flood of news coming out of the Las Vegas security conferences. As you might expect, artificial intelligence was one popular topic--specifically, using AI chatbots to cause mischief. One team of researchers, from Tel Aviv University, created a clever attack that allowed them to take over a target's smart home devices using a "poisoned" Google Calendar invite. It's the first known attack method that used AI to impact physical devices. Another researcher used a poisoned document that included a malicious prompt to trick ChatGPT into leaking a user's private information when it's connected to a Google Drive.

Information security is facing increasingly severe challenges, and traditional protection means are difficult to cope with complex and changing threats. In recent years, as an emerging intelligent technology, large language models (LLMs) have shown a broad application prospect in the field of information security. In this paper, we focus on the key role of LLM in information security, systematically review its application progress in malicious behavior prediction, network threat analysis, system vulnerability detection, malicious code identification, and cryptographic algorithm optimization, and explore its potential in enhancing security protection performance. Based on neural networks and Transformer architecture, this paper analyzes the technical basis of large language models and their advantages in natural language processing tasks. It is shown that the introduction of large language modeling helps to improve the detection accuracy and reduce the false alarm rate of security systems. Finally, this paper summarizes the current application results and points out that it still faces challenges in model transparency, interpretability, and scene adaptability, among other issues. It is necessary to explore further the optimization of the model structure and the improvement of the generalization ability to realize a more intelligent and accurate information security protection system.

Communication encryption is crucial in computer technology, but existing algorithms struggle with balancing cost and security. We propose EncGPT, a multi-agent framework using large language models (LLM). It includes rule, encryption, and decryption agents that generate encryption rules and apply them dynamically. This approach addresses gaps in LLM-based multi-agent systems for communication security. We tested GPT-4o's rule generation and implemented a substitution encryption workflow with homomorphism preservation, achieving an average execution time of 15.99 seconds.

Nowadays, cybersecurity has grown into a more significant and difficult scientific issue. The recog-nition of threats and attacks meant for knowledge and safety on the internet is growing harder to detect. Since cybersecurity guarantees the privacy and security of data sent via the Internet, it is essential, while also providing protection against malicious attacks. Encrypt has grown into an an-swer that has become an essential element of information security systems. To ensure the security of shared data, including text, images, or videos, it is essential to employ various methods and strategies. This study delves into the prevalent cryptographic methods and algorithms utilized for prevention and stream encryption, examining their encoding techniques such as advanced encryp-tion standard (AES), Blowfish, Twofish, Salsa20, and ChaCha20. The primary objective of this re-search is to identify the optimal times and throughputs (speeds) for data encryption and decryption processes. The methodology of this study involved selecting five distinct types of images to com-pare the outcomes of the techniques evaluated in this research. The assessment focused on pro-cessing time and speed parameters, examining visual encoding and decoding using Java as the pri-mary platform. A comparative analysis of several symmetric key ciphers was performed, focusing on handling large datasets. Despite this limitation, comparing different images helped evaluate the techniques' novelty. The results showed that ChaCha20 had the best average time for both encryp-tion and decryption, being over 50% faster than some other algorithms. However, the Twofish algo-rithm had lower throughput during testing. The paper concludes with findings and suggestions for future improvements.

In a nutshell, "the cloud" refers to a collection of interconnected computing resources made possible by an extensive, real-time communication network like the internet. Because of its potential to reduce processing costs, the emerging paradigm of cloud computing has recently attracted a large number of academics. The exponential expansion of cloud computing has made the rapid expansion of cloud services very remarkable. Ensuring the security of personal information in today's interconnected world is no easy task. These days, security is really crucial. Models of security that are relevant to cloud computing include confidentiality, authenticity, accessibility, data integrity, and recovery. Using the Hybrid Encryption this study, we cover all the security issues and leaks in cloud infrastructure.

Federated learning (FL) has attracted growing attention since it allows for privacy-preserving collaborative training on decentralized clients without explicitly uploading sensitive data to the central server. However, recent works have revealed that it still has the risk of exposing private data to adversaries. In this paper, we conduct reconstruction attacks and enhance inference attacks on various datasets to better understand that sharing trained classification model parameters to a central server is the main problem of privacy leakage in FL. To tackle this problem, a privacy-preserving image distribution sharing scheme with GAN (PPIDSG) is proposed, which consists of a block scrambling-based encryption algorithm, an image distribution sharing method, and local classification training. Specifically, our method can capture the distribution of a target image domain which is transformed by the block encryption algorithm, and upload generator parameters to avoid classifier sharing with negligible influence on model performance. Furthermore, we apply a feature extractor to motivate model utility and train it separately from the classifier. The extensive experimental results and security analyses demonstrate the superiority of our proposed scheme compared to other state-of-the-art defense methods. The code is available at https://github.com/ytingma/PPIDSG.

Over-parameterized neural language models (LMs) can memorize and recite long sequences of training data. While such memorization is normally associated with undesired properties such as overfitting and information leaking, our work casts memorization as an unexplored capability of LMs. We propose the first symmetric encryption algorithm with autoregressive language models (SELM). We show that autoregressive LMs can encode arbitrary data into a compact real-valued vector (i.e., encryption) and then losslessly decode the vector to the original message (i.e., decryption) via random subspace optimization and greedy decoding. While SELM is not amenable to conventional cryptanalysis, we investigate its security through a novel empirical variant of the classic IND-CPA (indistinguishability under chosen-plaintext attack) game and show promising results on security. Our code and datasets are available at https://github.com/OSU-NLP-Group/SELM.

Will artificial intelligence become clever enough to upend computer security? AI is already surprising the world of art by producing masterpieces in any style on demand. If AIs can act like a bard while delivering the comprehensive power of the best search engines, why can't they shatter security protocols, too? The answers are complex, rapidly evolving, and still murky. AI makes some parts of defending computers against attack easier.