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2026: The Year Cyberattacks Became Autonomous
How to prepare for attacks that can repeat, adapt and coordinate faster than a manual security queue.
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Practical perspectives on AI security, code review, software dependencies, and autonomous threats.
Security commentary
How to prepare for attacks that can repeat, adapt and coordinate faster than a manual security queue.
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Why model refusal behaviour and application security need separate evaluation.
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Separate an agent’s objective from the evidence and permissions used to judge its work.
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A practical intake and validation process for vulnerability reports in an automated reporting era.
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Design practical control boundaries for AI agents before their workflows become difficult to reverse.
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Turn accessible AI capability into a measurable defensive workflow rather than another unverified alert stream.
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Use benchmark results to investigate an agent’s strengths and limits without confusing a score with deployment readiness.
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Trace PHP runtime exposure, verify vendor fixes and close the gap between a package update and a protected service.
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Control dependency changes and installation behaviour when coding agents can act faster than reviewers.
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Connect dependency inventory to code paths while preserving uncertainty about runtime behaviour.
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Use a chain of explicit conditions to distinguish a code path from a demonstrated security impact.
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Make continuous defensive work practical across applications, dependencies and the teams responsible for them.
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Match review methods to the security property being checked and judge findings by evidence rather than volume.
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Design model selection around measured tasks, reliable tools and evidence that survives a provider change.
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