Opening — Why this matters now Phishing is no longer about bad grammar and suspicious links. It is about plausibility, tone, and timing. As attackers refine their craft, the detection problem quietly shifts from raw accuracy to judgment under uncertainty. That is precisely where today’s AI systems, despite their statistical confidence, begin to diverge from human reasoning. ...
In the high-stakes world of smart grids, digital substations have become both operational nerve centers and prime targets for cyberattacks. IEC61850-based communication, particularly GOOSE multicast messages, enables faster coordination but also introduces new vulnerabilities — especially for unmanned substations that rely heavily on remote access. Traditional anomaly detection systems (ADSs), while effective in standard IT contexts, falter here: they require continual retraining for each new threat and often struggle with scarce, imbalanced datasets. ...
When OpenAI released the open-weight model gpt-oss, it did something rare: before letting the model into the wild, its researchers pretended to be bad actors. This wasn’t an ethical lapse. It was a safety strategy. The team simulated worst-case misuse by fine-tuning gpt-oss to maximize its dangerous capabilities in biology and cybersecurity. They called this process Malicious Fine-Tuning (MFT). And the results offer something the AI safety debate sorely lacks: empirical grounding. ...
In today’s threat landscape, cybersecurity is no longer a battle of scripts and firewalls. It’s a war of minds. And with the rise of intelligent agents powered by Large Language Models (LLMs), we are now entering a new era where cyber defense becomes not just technical but deeply strategic. The paper “Game Theory Meets LLM and Agentic AI” by Quanyan Zhu provides one of the most profound frameworks yet for understanding this shift. ...