CBO Hack, Oracle Exploits Hit $50M Demand
Summary
Systemic underinvestment and cognitive biases are fueling high-profile cybersecurity failures. The U.S. CBO confirmed a breach, suspected to stem from an outdated Cisco ASA firewall lacking recent security updates, an issue amplified by government shutdowns delaying maintenance [1, 3]. In parallel, The Washington Post confirmed its data was compromised via Oracle’s E-Business Suite exploits used by the Clop ransomware gang, leading to a reported \(50 million demand [4](#article-4). This infrastructural decay contrasts sharply with advanced private sector funding: Terranova secured \)7 million in seed funding to deploy terraforming robots, estimating a \(92 million cost to lift 240 acres four feet in San Rafael, significantly undercutting the \)500 million cost of traditional seawalls 2. Meanwhile, technical overconfidence manifests in AI, mirroring the Dunning-Kruger Effect where users confidently produce nonsensical outputs, similar to the lime juice robbers of 1995 5. This juxtaposition highlights a critical disconnect between necessary security upkeep and speculative engineering capital.
Key Moments
-
The CBO confirmed the intrusion exploited an outdated Cisco ASA firewall, highlighting gaps in foundational patching activities.
— Article [3] -
The Washington Post breach via Oracle E-Business Suite led to Clop ransomware demanding $50 million from one affected entity.
— Article [4] -
Terranova secured $7 million seed funding to deploy robots, estimating $92 million to lift 240 acres four feet in San Rafael.
— Article [2] -
AI confidence without competence is described as 'Dunning-Kruger as a service,' referencing historical examples like the lime juice robbers.
— Article [5] -
The government shutdown directly delayed essential security work for agencies like the CBO, increasing immediate risk exposure.
— Article [1]
Different Perspectives
Supporting View
The technological overconfidence prevalent in AI output is directly analogous to the Dunning-Kruger effect, where low-skill actors overestimate their competence.