The gap between open and closed AI models in cyber capabilities keeps shrinking
Source: The Decoder (opens in a new tab) · Matthias Bastian
Intel Summary
In testing on the Vals AI benchmark, OpenAI's GPT-6 Sol Daybreak achieved 80.8 percent of attack objectives within simulated corporate networks. Concurrently, the open-weight model DeepSeek V4.1 Flash achieved a 72.8 percent success rate at one-tenth the cost, narrowing the capability disparity between proprietary and unrestricted models.
Why It Matters
High offensive success rates in open-weight models significantly lower the cost and barrier to entry for automated network attacks. Because open-weight distributions cannot be centrally revoked or restricted after release, enterprise security teams face an accelerating requirement to upgrade defensive postures against accessible automated offensive tools.
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