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Hackers are often early pioneers in using new technology, and in the field of AI it's no different. Although very few hackers are designing their own LLMs - they are making active use of AI as part of an existing attack pattern which then becomes much more difficult to spot and mitigate. The new AI techniques are combined with the old attack patterns, often with devastating effect. We look at some of the most common threats below.
By combining an existing successful attack pattern with different elements and techniques used by AI, the attackers often have a far more powerful weapon, that avoids traditional detection. In order to combat these new threats, we must also use multiple techniques aimed at understanding the new multiple domain attack vendor chaining sequences in play. For this, we fight fire with fire, by using agentic AI to pick up the chained threat types, using multiple agents to collect, consolidate, and assess the threat data.
How we use Agentic AI
New AI techniques can bypass traditional defences, creating an opportunity to exploit a vulnerability. VerifiedThreat deploys smart agents that can learn from the context, and combine with each other to understand the new breach possibilities. Traditional cybersecurity defence often relies on the first layer of defences that can be easily breached. For example, a site may be protected with both geo-location IP blocking and CAPTCHA services to prevent unwanted automated bot traffic from hitting the site. As a result of implementing these defences the bot traffic will dramatically decrease. Looks like problem solved. However, as we've seen above, its relatively trivial to bypass both the IP geo blocking with local proxies, hiding in the local mobile ASN range, and bypassing CAPTCHA. This attack type won't be picked up, the site is assumed to be same. Combing multiple agents to systematically search for vulnerabilities can greatly help to stop these multi-chain attacks.
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