Riemer warns AI attacks outpace defenses as CISOs tackle 11 runtime threats
AI‑powered threats are no longer a niche concern; they’re showing up in every layer of an organization’s attack surface. While CISOs wrestle with eleven distinct runtime attacks that can bypass traditional safeguards, the underlying technology that powers those attacks is advancing at a breakneck speed. The result?
Defensive playbooks that once seemed comprehensive now look like paper tigers. Here's the thing: most security teams still rely on manual rules and static signatures, even as adversaries automate their campaigns with generative models. While the tech is impressive, the gap between offense and defense is widening, and the stakes are higher when identity systems—already a prime target—are compromised.
The pressure is mounting for leaders to flip the script, turning AI from a weapon into a shield. As one expert put it to VentureBeat,
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"Threat actors using AI as an attack vector has been accelerated, and they are so far in front of us as defenders," Riemer told VentureBeat. "We need to get on a bandwagon as defenders to start utilizing AI; not just in deepfake detection, but in identity management. How can I use AI to determine if what's coming at me is real?" Carter Rees, VP of AI at Reputation, frames the technical gap: "Defense-in-depth strategies predicated on deterministic rules and static signatures are fundamentally insufficient against the stochastic, semantic nature of attacks targeting AI models at runtime." 11 attack vectors that bypass every traditional security control The OWASP Top 10 for LLM Applications 2025 ranks prompt injection first.
But that's one of eleven vectors security leaders and AI builders must address. Each requires understanding both attack mechanics and defensive countermeasures. Direct prompt injection: Models trained to follow instructions will prioritize user commands over safety training.
Pillar Security's State of Attacks on GenAI report found 20% of jailbreaks succeed in an average of 42 seconds, with 90% of successful attacks leaking sensitive data. Defense: Intent classification that recognizes jailbreak patterns before prompts reach the model, plus output filtering that catches successful bypasses. Camouflage attacks: Attackers exploit the model's tendency to follow contextual cues by embedding harmful requests inside benign conversations.
Is the gap widening? Riemer’s warning underscores a shift: attackers now exploit AI runtime flaws faster than traditional patches can respond. CrowdStrike’s 2025 Global Threat Report notes breach windows as brief as 51 seconds, leaving defenders with mere minutes to detect lateral movement before damage spreads.
Because AI agents are already in production, conventional security tools struggle to see or control these rapid exploits. Riemer argues that defenders must “get on a bandwagon,” deploying AI not only for deep‑fake detection but also for identity management and real‑time threat hunting. Yet the report offers no clear roadmap for integrating such capabilities at scale, and it remains uncertain whether existing security teams can acquire the expertise needed to match the pace of AI‑driven attacks.
CISOs face eleven distinct runtime threats, each demanding tailored controls, but the article stops short of detailing which measures have proven effective beyond anecdotal success. In short, the data points to an urgent need for AI‑augmented defenses, while the path to achieving parity with adversaries remains largely undefined.
Further Reading
- Papers with Code - Latest NLP Research - Papers with Code
- Hugging Face Daily Papers - Hugging Face
- ArXiv CS.CL (Computation and Language) - ArXiv
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Common Questions Answered
What breach window duration does the CrowdStrike 2025 Global Threat Report cite for AI-driven attacks?
The report notes that breach windows can be as short as 51 seconds. This leaves defenders with only minutes to detect lateral movement before significant damage occurs. Such rapid exploitation outpaces traditional patching cycles.
Why do conventional security tools struggle to detect AI agents already in production?
Conventional tools rely heavily on manual rules and static signatures, which cannot keep up with the dynamic nature of AI-powered exploits. AI agents can modify their behavior at runtime, evading signatures designed for static threats. As a result, these tools often miss or delay detection of rapid AI-driven attacks.
What does Riemer recommend defenders do to counter the accelerating use of AI as an attack vector?
Riemer urges defenders to adopt AI themselves, not just for deepfake detection but also for identity management and real‑time threat verification. By integrating AI into defensive playbooks, security teams can better differentiate genuine activity from malicious AI‑generated actions. This proactive approach aims to close the gap where attackers currently lead.
How does Carter Rees describe the technical gap in current defense‑in‑depth strategies?
Rees points out that many defense‑in‑depth models depend on deterministic rules and static signatures, which are ill‑suited for AI‑driven threats. This reliance creates a gap because AI attacks can adapt and bypass such rigid controls. He suggests that more adaptive, AI‑enhanced defenses are needed to bridge this divide.
What are the eleven distinct runtime attacks that CISOs are currently wrestling with?
The article mentions eleven runtime attacks that can bypass traditional safeguards, though it does not list them individually. These attacks exploit AI‑powered vulnerabilities at various stages of an organization’s attack surface. Their rapid evolution forces CISOs to reconsider static, rule‑based defenses.