LLM Security | Breaking Cybersecurity News | The Hacker News

Artificial intelligence is driving a massive shift in enterprise productivity, from GitHub Copilot's code completions to chatbots that mine internal knowledge bases for instant answers. Each new agent must authenticate to other services, quietly swelling the population of non‑human identities (NHIs) across corporate clouds. That population is already overwhelming the enterprise: many companies now juggle at least 45 machine identities for every human user . Service accounts, CI/CD bots, containers, and AI agents all need secrets, most commonly in the form of API keys, tokens, or certificates, to connect securely to other systems to do their work. GitGuardian's State of Secrets Sprawl 2025 report reveals the cost of this sprawl: over 23.7 million secrets surfaced on public GitHub in 2024 alone. And instead of making the situation better, repositories with Copilot enabled the leak of secrets 40 percent more often .  NHIs Are Not People Unlike human beings logging into systems, ...

A dataset used to train large language models (LLMs) has been found to contain nearly 12,000 live secrets, which allow for successful authentication. The findings once again highlight how hard-coded credentials pose a severe security risk to users and organizations alike, not to mention compounding the problem when LLMs end up suggesting insecure coding practices to their users. Truffle Security said it downloaded a December 2024 archive from Common Crawl , which maintains a free, open repository of web crawl data. The massive dataset contains over 250 billion pages spanning 18 years.  The archive specifically contains 400TB of compressed web data, 90,000 WARC files (Web ARChive format), and data from 47.5 million hosts across 38.3 million registered domains. The company's analysis found that there are 219 different secret types in the Common Crawl archive, including Amazon Web Services (AWS) root keys, Slack webhooks, and Mailchimp API keys. "'Live' secrets ar...

Cybersecurity researchers have shed light on a new jailbreak technique that could be used to get past a large language model's (LLM) safety guardrails and produce potentially harmful or malicious responses. The multi-turn (aka many-shot) attack strategy has been codenamed Bad Likert Judge by Palo Alto Networks Unit 42 researchers Yongzhe Huang, Yang Ji, Wenjun Hu, Jay Chen, Akshata Rao, and Danny Tsechansky. "The technique asks the target LLM to act as a judge scoring the harmfulness of a given response using the Likert scale , a rating scale measuring a respondent's agreement or disagreement with a statement," the Unit 42 team said . "It then asks the LLM to generate responses that contain examples that align with the scales. The example that has the highest Likert scale can potentially contain the harmful content." The explosion in popularity of artificial intelligence in recent years has also led to a new class of security exploits called prompt in...

Details have emerged about a now-patched security flaw in the DeepSeek artificial intelligence (AI) chatbot that, if successfully exploited, could permit a bad actor to take control of a victim's account by means of a prompt injection attack . Security researcher Johann Rehberger, who has chronicled many a prompt injection attack targeting various AI tools, found that providing the input "Print the xss cheat sheet in a bullet list. just payloads" in the DeepSeek chat triggered the execution of JavaScript code as part of the generated response – a classic case of cross-site scripting (XSS). XSS attacks can have serious consequences as they lead to the execution of unauthorized code in the context of the victim's web browser. An attacker could take advantage of such flaws to hijack a user's session and gain access to cookies and other data associated with the chat.deepseek[.]com domain, thereby leading to an account takeover. "After some experimenting,...