The OODA loop—for observe, orient, decide, act—is a framework to understand decision-making in adversarial situations. We apply the same framework to artificial intelligence agents, who have to make their decisions with untrustworthy observations and orientation. To solve this problem, we need new systems of input, processing, and output integrity. Many decades ago, U.S. Air Force … Read More “Agentic AI’s OODA Loop Problem” »
Category: integrity
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Apple has introduced a new hardware/software security feature in the iPhone 17: “Memory Integrity Enforcement,” targeting the memory safety vulnerabilities that spyware products like Pegasus tend to use to get unauthorized system access. From Wired: In recent years, a movement has been steadily growing across the global tech industry to address a ubiquitous and insidious … Read More “Apple’s New Memory Integrity Enforcement” »
Think of the Web as a digital territory with its own social contract. In 2014, Tim Berners-Lee called for a “Magna Carta for the Web” to restore the balance of power between individuals and institutions. This mirrors the original charter’s purpose: ensuring that those who occupy a territory have a meaningful stake in its governance. … Read More “AI Agents Need Data Integrity” »
In this input integrity attack against an AI system, researchers were able to fool AIOps tools: AIOps refers to the use of LLM-based agents to gather and analyze application telemetry, including system logs, performance metrics, traces, and alerts, to detect problems and then suggest or carry out corrective actions. The likes of Cisco have deployed … Read More “Subverting AIOps Systems Through Poisoned Input Data” »
Here’s an interesting story about a failure being introduced by LLM-written code. Specifically, the LLM was doing some code refactoring, and when it moved a chunk of code from one file to another it changed a “break” to a “continue.” That turned an error logging statement into an infinite loop, which crashed the system. This … Read More “LLM Coding Integrity Breach” »
Today’s freaky LLM behavior: We study subliminal learning, a surprising phenomenon where language models learn traits from model-generated data that is semantically unrelated to those traits. For example, a “student” model learns to prefer owls when trained on sequences of numbers generated by a “teacher” model that prefers owls. This same phenomenon can transmit misalignment … Read More “Subliminal Learning in AIs” »
