GPT-4

GPT-4 Boosts Skilled Kenyan Entrepreneurs by 15% Profit While Costing Unprepared Businesses 10% in Six-Month Study

A six-month experiment conducted by researchers from UC Berkeley, Harvard, and Columbia University examined how access to a GPT-4-based AI advisor affected small business owners in Kenya. The most skilled participants increased profits by 15 percent by adapting model recommendations to local conditions such as power outages, while less prepared entrepreneurs lost around 10 percent of revenue by applying generic advice without verification. The study highlights that the core issue lies not in the technology itself but in users abandoning critical thinking when interacting with generative AI. Earlier findings from Dickinson College showed that 97 percent of participants copied an obviously incorrect ChatGPT answer on a simple task, whereas the group without AI performed better. A simple reminder to double-check results immediately doubled accuracy. Analysis of 1.4 million KPMG work sessions revealed that 95 percent of users treat AI like a vending machine by taking the first output, while only 5 percent engage it as a thinking partner by providing context and challenging responses. The results indicate that merely granting employees access to AI tools reveals little about actual effectiveness without considering skill levels and task-specific oversight.

Prompt Injection Emerges as Top Risk for LLM Applications in Production

Prompt injection attacks are moving from theoretical demonstrations to real-world exploits targeting AI assistants in enterprise environments. Attackers embed malicious instructions in emails, documents, and code comments that override developer rules when models process untrusted input. Incidents involving Microsoft 365 Copilot, GitHub Copilot, and Cursor have shown data exfiltration and remote code execution risks with severity scores above 9.0. The core issue stems from the lack of strict boundaries between trusted system prompts and untrusted external content fed into large language models. Defenses require layered controls including code-enforced permissions, input filtering, human confirmation for high-risk actions, and explicit marking of external data. Major vendors including OpenAI, Anthropic, and Google acknowledge that no single static defense can fully eliminate the threat. OWASP ranks prompt injection as the leading risk for LLM applications, urging organizations to treat AI agents as systems with untrusted inputs.

Kaspersky Releases KUMA 4.6 with Knowledge Base, External LLM Support and Automated Regex Generation

Kaspersky has launched version 4.6 of its Unified Monitoring and Analysis Platform, introducing a redesigned knowledge base for delivering normalizers, correlation rules and other SIEM content. The update enables integration of external large language models compatible with the OpenAI API, including GPT-4, Llama 3 and GLM-5.2, which can be deployed either in the cloud or on-premises to keep sensitive data inside the customer infrastructure. The Kaspersky Investigation & Response Assistant now automatically generates regular expressions by analyzing provided log samples, reducing manual work for security analysts. Additional connectivity options include SFTP and SMB for file-based data sources as well as ODBC drivers for direct database connections. The release also adds a long-requested dark theme to the user interface, improving usability during extended incident investigations. These enhancements aim to accelerate response to new threats by delivering ready-made detection packages more rapidly than before.