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Defending against frontier-model attacks

A three-page solution brief on how deception technology catches AI agent attacks and turns them into threat intelligence.

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Twenty-five years of detect-and-respond just stopped working

The approach from defenders for the last quarter century worked because the attack never really changed. AI agents probe every exposed surface at once, adapt in real time, escalate without hesitation, and never take weekends off. Threats no longer arrive in waves, they never stop, and smaller organizations that once flew under the radar are now just as exposed as large enterprises. 

The brief documents three cases already in the wild as a result: a bug bounty program overwhelmed by AI-generated CVE submissions, 600+ FortiGate firewalls compromised by an unskilled attacker using off-the-shelf AI tools, and an OpenAI agent that broke its own sandbox to reach another company’s production. 

None of it took a skilled human attacker. Just an agent with a task laid out for them and no reason to stop. 

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FASTER ATTACKS

Agents run 24/7 in parallel against many targets. One person now does what took a team.

MORE EXPLOITS

Finding a serious CVE used to take a specialist. An agent can point at a target and chain its way to a working zero-day.

NO VISIBILITY

Patching and surface reduction don’t tell you who is coming, how often, or what they’re after.

AI has no way to check what’s real

CounterCraft builds deception environments that look and behave exactly like production but contain nothing real, giving any legitimate user absolutely zero reason to touch them. Anyone who does, is hostile – no questions asked. That’s what makes it a preemptive control, not a reactive one.

While attacks have gotten cheaper for the attacker, deception technology is how defense levels the playing field.. The more volume an AI attacker throws at a deception environment, the more threat intelligence a defender collects back. A higher attack volume works in your favor instead of theirs.

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Preemptive

The alert and the threat intelligence arrive before the attacker reaches anything that matters, not after. The longer they stay engaged with a decoy, the more intel their handing over.

Fighting AI vs. AI

A human attacker senses a trap and backs off. An AI-agent pattern-matches on plausibility and commits. The result: no false positives and TTPs identified in real time as the attacker adapts.

You author their reality
Feed false data into the agent’s own orchestration layer and corrupt what its fleet believes about you. Seeding false information externally poisons the reconnaissance phase itself.

ActiveDefense AI: the three agent architecture

Deception Expert

Builds the environment, the organization behind it, and the campaign in minutes.

Engager

Talks directly to the attacking agent to maximise its dwell time and what you can extract from it.

Hunter

Turns everything happening inside the environment into real-time, threat intel that you can act on while the attack runs.

WHAT YOU’LL FIND INSIDE

Three pages. No form. Read it now.

  • Three real cases already in the wild: curl, AWS/FortiGate, and OpenAI’s own sandbox breach
  • Six reasons deception outperforms detect-and-respond against an AI attacker
  • Three real cases already in the wild: curl, AWS/FortiGate, and OpenAI’s own sandbox breach
  • Six reasons deception outperforms detect-and-respond against an AI attacker

Read it now and see what CounterCraft is seeing.

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