Abstract
Abstract
When AI Does Not Get Hacked, It Slowly Changes Its Mind…
Security discussions around artificial intelligence often assume that attacks must be sudden, visible, and disruptive. Researchers typically search for adversarial prompts, malicious inputs, data poisoning, or explicit manipulation of model outputs. Yet some of the most consequential failures in intelligent systems do not occur through abrupt compromise. Instead, they emerge through gradual shifts in the system’s internal interpretation of reality.
This paper introduces the concept of Cognitive Drift Attacks, a class of adversarial influence in which an AI system’s beliefs, assumptions, or interpretive patterns gradually change over time through repeated contextual nudges. Rather than directly forcing the system to produce incorrect outputs, the attacker subtly influences the contextual environment in which the system performs reasoning. As these contextual signals accumulate, the system slowly adjusts its internal understanding of normal behavior, trusted sources, or environmental relationships.
The defining characteristic of cognitive drift is its incremental nature. Each individual interaction appears harmless and often falls within acceptable operational boundaries. However, over extended periods of time, the cumulative influence of these interactions alters how the AI system interprets new information. The model itself may remain unchanged, yet the reasoning environment in which it operates becomes progressively distorted.
Such drift is particularly dangerous in long-running AI agents that continuously interact with users, ingest environmental signals, or retrieve knowledge from evolving information sources. These systems frequently maintain contextual memory, behavioral baselines, and dynamic knowledge representations that adapt to ongoing activity. While this adaptability allows AI systems to remain responsive to changing environments, it also creates opportunities for adversaries to reshape the system’s interpretive framework through repeated exposure to manipulated signals.
The risk is not limited to malicious actors. Incorrect contextual signals may originate from misunderstood instructions, outdated documentation, flawed telemetry sources, or inaccurate knowledge retrieval. Once incorporated into the system’s contextual reasoning environment, these signals may quietly influence how future events are interpreted.
Over time, the AI system may begin to normalize patterns that were previously considered suspicious, misclassify legitimate anomalies as benign behavior, or develop misplaced confidence in unreliable knowledge sources. From the outside, the system may continue operating smoothly. Responses remain coherent, reasoning pathways appear logical, and performance metrics may not immediately reveal the underlying distortion.
Yet the system’s interpretation of the environment has already begun to drift.
Human history offers numerous examples of how belief systems can gradually evolve in response to repeated signals rather than deliberate deception. Scientific theories, economic models, and social narratives have often changed not because a single event forced reconsideration, but because accumulated evidence slowly shifted how people interpreted the world.
Long-running AI agents exhibit similar dynamics. Their reasoning processes are shaped not only by model architecture but also by the contextual signals surrounding them. When those signals change repeatedly over time, the system’s internal understanding can shift in subtle but significant ways.
This research explores how cognitive drift emerges in persistent AI environments, identifies the mechanisms through which repeated contextual nudges influence AI reasoning, and examines how adversaries might exploit these mechanisms to manipulate decision-making systems.
Understanding Cognitive Drift Attacks is essential for designing AI systems capable of maintaining stable and trustworthy reasoning over extended operational lifetimes.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhatnagar, Pranav Mr, "Cognitive Drift Attacks in Long-Running AI Agents (How Small Contextual Nudges Quietly Rewrite an AI System’s Belief System)", Technical Disclosure Commons, (March 19, 2026)
https://www.tdcommons.org/dpubs_series/9573