Natural Language Processing | Breaking Cybersecurity News | The Hacker News

DALL-E for coders? That's the promise behind vibe coding, a term describing the use of natural language to create software. While this ushers in a new era of AI-generated code, it introduces "silent killer" vulnerabilities: exploitable flaws that evade traditional security tools despite perfect test performance. A detailed analysis of secure vibe coding practices is available here . TL;DR: Secure Vibe Coding Vibe coding, using natural language to generate software with AI, is revolutionizing development in 2025. But while it accelerates prototyping and democratizes coding, it also introduces "silent killer" vulnerabilities: exploitable flaws that pass tests but evade traditional security tools. This article explores: Real-world examples of AI-generated code in production Shocking stats: 40% higher secret exposure in AI-assisted repos Why LLMs omit security unless explicitly prompted Secure prompting techniques and tool comparisons (GPT-4, Claude, Cursor, etc.) Reg...

Cybersecurity researchers have discovered a novel attack technique called TokenBreak that can be used to bypass a large language model's (LLM) safety and content moderation guardrails with just a single character change. "The TokenBreak attack targets a text classification model's tokenization strategy to induce false negatives, leaving end targets vulnerable to attacks that the implemented protection model was put in place to prevent," Kieran Evans, Kasimir Schulz, and Kenneth Yeung said in a report shared with The Hacker News. Tokenization is a fundamental step that LLMs use to break down raw text into their atomic units – i.e., tokens – which are common sequences of characters found in a set of text. To that end, the text input is converted into their numerical representation and fed to the model.  LLMs work by understanding the statistical relationships between these tokens, and produce the next token in a sequence of tokens. The output tokens are detokeni...

Recently the Office of the Director of National Intelligence (ODNI) unveiled a new strategy for open-source intelligence (OSINT) and referred to OSINT as the "INT of first resort". Public and private sector organizations are realizing the value that the discipline can provide but are also finding that the exponential growth of digital data in recent years has overwhelmed many traditional OSINT methods. Thankfully, Artificial Intelligence (AI) and Machine Learning (ML) are starting to provide a transformative impact on the future of information gathering and analysis.  What is Open-Source Intelligence (OSINT)? Open-Source Intelligence refers to the collection and analysis of information from publicly available sources. These sources can include traditional media, social media platforms, academic publications, government reports, and any other data that is openly accessible. The key characteristic of OSINT is that it does not involve covert or clandestine methods of information ga...