The AI Blind Spot: Why We're Ignoring the Most Dangerous Risks

AI governance frameworks and executive risk perception have a critical blind spot: they ignore documented risks of overreliance and lack of robustness despite known attack techniques targeting these weaknesses.

What most people think

The primary AI risks being actively discussed and managed are privacy, security, fairness, and multi-agent interactions. Organizations focus on these categories because they're visible, well-defined, and have clear regulatory implications. Privacy violations, security breaches, and fairness concerns make headlines and attract regulatory attention. This creates a perception that these are the most significant AI risks organizations face today.

What the data shows

Our live incident database, which tracks reported AI failures from public news, contains 1227 stories in the last 180 days. Yet when we categorize these stories by risk type according to the MIT AI Risk Repository, two critical risk classes appear with zero stories: 5.1 Overreliance and unsafe use and 7.3 Lack of capability or robustness.

The risk categories that dominate reporting are:

• 2.1 Compromise of privacy by leaking or inferring sensitive information: 154 stories
• 7.6 Multi-agent risks: 91 stories
• 2.2 AI system security vulnerabilities and attacks: 88 stories
• 4.3 Fraud, scams, and targeted manipulation: 79 stories
• 6.5 Governance failure: 41 stories

Meanwhile, documented attack techniques like Evade AI Model (AML.T0015) with 18 real cases and AI-Enabled Product or Service (AML.T0047) with 16 real cases could lead to overreliance or robustness failures but are not reflected in incident categories.

Why this happens

The mechanism is straightforward: governance frameworks lack the mechanisms or incentives to track and report failures related to overreliance and robustness. These incidents often don't have clear victims or immediate regulatory triggers. When an AI system fails due to overreliance, the consequences may be diffuse or indirect, making them less likely to be reported as discrete incidents. Additionally, organizations may not recognize these failures as distinct from other operational issues.

The best argument against this

Some might argue that these risks are simply being captured under existing categories like "security" or "safety." However, the specificity of the MIT AI Risk Repository classification and the documented attack techniques targeting overreliance and robustness demonstrate these are distinct failure modes requiring specific attention.

What I think happens next

By June 2028, at least one major incident involving 'Overreliance and unsafe use' (5.1) or 'Lack of capability or robustness' (7.3) will occur and be widely reported in mainstream outlets, forcing a re-evaluation of current AI governance frameworks. This would be proven wrong if, by that date, no major incident related to these risk classes appears in the top five reporting outlets according to our live AI incident database.

What to do about it

• Incorporate specific controls and monitoring for system robustness and user overreliance into internal risk assessments and audits.
• Advocate for the inclusion of risk classes 5.1 and 7.3 in future AI governance reporting standards and frameworks.
• Implement regular testing for overreliance scenarios and system robustness, similar to how penetration testing is conducted for security vulnerabilities.
• Train staff to recognize and report signs of AI overreliance and system limitations.
• Consider threat intelligence platforms like threatclaw.ai for understanding potential attack vectors targeting system robustness and user overreliance.

More from our platforms

These sister platforms cover the parts of this problem that sit outside governance.

• Argus (argus.threatclaw.ai) records every trace an AI application produces and scans it for prompt injection, jailbreaks and data leaks, including the attacks hidden inside retrieved documents and tool results rather than in what the user typed. Governance decides what an AI agent is allowed to do. Argus shows what it actually did.
• ThreatClaw (www.threatclaw.ai) tracks the threat side of the same systems: 22 live intelligence feeds, exploitation predicted before it is officially confirmed, threat actor profiles, and detection rules you can deploy straight away. A control is only as good as the threat it is sized against.

Related reading:

• When Your AI Fails, Who Do You Tell? The New Rules Are Here on ThreatClaw
• The AI Management Certificate That Changes Your Security Job on ThreatClaw