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John P. Mello Jr.'s coverage predominantly revolves around software and SaaS, with a focus on security and technology trends. His work tends to include citations of industry-specific data and expert commentary, so he is likely receptive to pitches offering in-depth analysis or exclusive insights into the latest developments in software security, particularly related to programming languages, supply chain security, threat modeling, and relevant tools.
Given his interest in citing data and including expert commentary, consider providing him with access to new research findings or connecting him with subject matter experts who can offer unique perspectives on emerging technologies or recent events within the realm of software development and cybersecurity.
While John does not have a specific geographic focus mentioned for his articles, it's important to note that he covers topics relevant internationally within the context of software & SaaS.
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By: John P. Mello Jr.
A new interactive tool for learning about securing generative AI models called Chat Playground has been launched by the OWASP Gen AI Security Project. Steve Wilson, co-chair of the Gen AI Security Project, said that group wanted to provide something with a low bar to getting started — and with Chat Playground, testing teams only need a web browser.
By: John P. Mello Jr.
A new guide published by the Cloud Security Alliance (CSA) gives red teams some useful guidance on how to go about securing agentic AI systems. Red-teaming for agentic AI requires a specialized approach because the planning, reasoning, tool utilization, and autonomous capabilities of those systems create attack surfaces and failure modes that extend far beyond those present in standard large language models (LLMs) or generative AI models, the CSA guide highlights.
By: John P. Mello
A new vulnerability database launched by the European Union could shake up an ecosystem long dominated by the United States.
By: John P. Mello Jr.
While the shortest distance between two points is a straight line, a straight-line attack on a large language model isn't always the most efficient — and least noisy — way to get the LLM to do bad things. That's why malicious actors have been turning to indirect prompt injection attacks on LLMs.
