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Scooped by
JC Gaillard
onto Artificial Intelligence and Cybersecurity October 3, 2:55 AM
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LLMs are software systems requiring proper containment control, not sentient actors with malicious intent.
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The combination of autonomy and speed is a potent brew that can expose gaps in security programs today.
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Measuring AI model security gets complex, fast, thanks to a growing patchwork of benchmarks targeting different variables in the equation.
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The more significant impact of AI is that it is exposing a weakness that has existed within vulnerability management programs for years: severity has never been the same thing as risk.
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AI agent permissions outrun controls: 70% of organizations let agents act without human sign-off, but only 49% can audit every agent action.
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Stolen AI API keys are powering an autonomous hacking tool sold on criminal forums, Bitsight reports, as AI agent chatter jumped 67 percent.
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Agentic systems are exposing years of unresolved data debt by turning ambiguity into operational exposure.
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AI vulnerability patching tools can stop a crash without fixing the bug, and benchmark scores often miss the difference, researchers found.
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Just over half of 200 leaders in ArmorCode's vulnerability management survey expect to struggle with program complexity if nothing changes.
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AI is widening the gap between the CVEs teams find and the ones they can fix. Closing it means asking which flaws attackers can actually reach.
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AI and quantum are both emerging technologies, and are both challenging established cyber benchmarks daily.
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More than 80,000 proxy servers have been observed cloaking the origin of traffic to frontier models, likely using AI credentials harvested via information stealers, phishing campaigns, and supply-chain attacks.
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Frontier labs OpenAI and Anthropic oversold their 'rogue AI' hacks to push for federal regulations that would disadvantage open models, competitors claim.
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CLOSEDQUORUM queries multiple models, tallies their decisions and automatically executes the winning action, eliminating the need for continuous attacker commands.
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Teams should know: Within these boundaries, proceed; cross them, and the use case comes back for review.
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Here's how validation answers the questions boards have about cyber risk.
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AI is affecting cybersecurity jobs and career development as employers cut roles and seek skills to manage threats and check tools.
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Unsanctioned AI use is epidemic, and agentic AI expands the risk. Here are four proven tactics that work better together.
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AI-powered penetration testing can uncover complex attack paths, but safety restrictions and network limitations leave many assets and vulnerabilities untested.
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Non-human identities now outnumber human accounts 45 to 1, leaving critical security gaps. Limit the vulnerabilities and enact governance with these tips.
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With flaw discoveries mounting, adversarial simulation and continuous pen testing can help CISOs identify which vulnerabilities matter most. New autonomous tooling is here to lend a hand but skilled practitioners remain critical.
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Enterprises regularly rigorously monitor human employees, while autonomous AI agents quietly operate with broad privileges.
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As AI accelerates vulnerability discovery and exploitation, so-called virtual patching still comes down to defense-in-depth and strong application security fundamentals.
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LLMs are software systems requiring proper containment control, not sentient actors with malicious intent.
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Crisis management experts say that AI is vital tool for stress-testing crisis management plans that can reveal critical blind spots that human teams may overlook.
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Software has never been easier to build, or easier to get wrong at scale.
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