Topic starter
27/06/2026 1:07 pm
TL;DR: Generative AI is being used by attackers to increase attack success rates and to power malicious LLMs such as WormGPT, according to Abnormal AI’s Vision 2024 webinar with Keren Elazari. The governing assumption is collapsing because cybercrime now adapts at AI speed, while traditional detection and user-awareness controls still assume slower, more predictable attacker behaviour.
Editorial analysis by NHI Mgmt Group, based on content published by Abnormal AI: “AI, Cybersecurity, and the Promise of Tomorrow: A Hacker's Insight”.
Key questions
Q: How should security teams reduce phishing risk when AI makes scam messages more convincing?
A: Teams should stop relying on obvious spelling mistakes and train people to verify the sender, destination, and request through a separate channel.
Q: Why do AI-assisted cyberattacks reduce the value of static awareness training?
A: Static awareness training assumes attackers reuse predictable language and patterns.
Practitioner guidance
- Harden email and identity trust points Reassess the controls that decide whether a message, login attempt, or workflow request should be trusted when the content is AI-generated and highly variable.
- Update detection for AI-assisted variation Tune detections to look for campaign behaviour, intent, and abnormal interaction patterns rather than fixed phrasing or repeated lures.
- Train users on AI-shaped social engineering Refresh awareness content so staff can recognise highly polished, personalised, and rapidly reworded attempts instead of obvious spam cues.
Bottom line: Generative AI is changing cybercrime by improving the quality and speed of attacker output, which weakens controls built around predictable, repetitive threats.
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This topic was modified 4 days ago by NHI Mgmt Group
27/06/2026 2:18 pm
Generative AI has become an attack multiplier, not a separate threat class: The article shows that the primary change is speed, scale, and quality in attacker output. That matters because many security programmes still tune controls to catch repetitive, low-quality fraud or phishing artefacts. The practitioner conclusion is that detection quality has to rise as attacker generation quality rises.
A question worth separating out:
Q: What should teams do first when malicious LLMs start appearing in their threat model?
A: Start by identifying the workflows where generated content could influence trust, such as email, credential prompts, support processes, and payment approvals. Then tighten controls at those decision points so a convincing message cannot move an identity workflow forward on appearance alone.
👉 Read our full editorial: Generative AI is reshaping cybercrime tactics and attack success
This post was modified 4 days ago by NHI Mgmt Group