Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis
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| Main Authors: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917113395937280 |
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| author | Beltz, Brandon Chen, Po-Yu Doty, James Fonken, Yvonne Gurney, Nikolos Hsing, Hsiang-Wen Hirschmann, Sofia Israelsen, Brett Lau, Nathan Li, Mengyun Marsella, Stacy Murray, Michael Oh, Jinwoo Sliva, Amy Srivastava, Kunal Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan |
| author_facet | Beltz, Brandon Chen, Po-Yu Doty, James Fonken, Yvonne Gurney, Nikolos Hsing, Hsiang-Wen Hirschmann, Sofia Israelsen, Brett Lau, Nathan Li, Mengyun Marsella, Stacy Murray, Michael Oh, Jinwoo Sliva, Amy Srivastava, Kunal Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan |
| contents | This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new defensive surface. Conventional cyber defenses assume rational, utility-maximizing attackers, yet real-world adversaries exhibit cognitive constraints and biases that shape their interactions with complex digital systems. GAMBiT embeds insights from cognitive science into cyber environments through cognitive triggers, which activate biases such as loss aversion, base-rate neglect, and sunk-cost fallacy, and through newly developed cognitive sensors that infer attackers' cognitive states from behavioral and network data. Three rounds of human-subject experiments (total n=61) in a simulated small business network demonstrate that these manipulations significantly disrupt attacker performance, reducing mission progress, diverting actions off the true attack path, and increasing detectability. These results demonstrate that cognitive biases can be systematically triggered to degrade the attacker's efficiency and enhance the defender's advantage. GAMBiT establishes a new paradigm in which the attacker's mind becomes part of the battlefield and cognitive manipulation becomes a proactive vector for cyber defense. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_00098 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis Beltz, Brandon Chen, Po-Yu Doty, James Fonken, Yvonne Gurney, Nikolos Hsing, Hsiang-Wen Hirschmann, Sofia Israelsen, Brett Lau, Nathan Li, Mengyun Marsella, Stacy Murray, Michael Oh, Jinwoo Sliva, Amy Srivastava, Kunal Trent, Stoney Wu, Peggy Yang, Ya-Ting Zhu, Quanyan Cryptography and Security Computer Science and Game Theory This paper introduces GAMBiT (Guarding Against Malicious Biased Threats), a cognitive-informed cyber defense framework that leverages deviations from human rationality as a new defensive surface. Conventional cyber defenses assume rational, utility-maximizing attackers, yet real-world adversaries exhibit cognitive constraints and biases that shape their interactions with complex digital systems. GAMBiT embeds insights from cognitive science into cyber environments through cognitive triggers, which activate biases such as loss aversion, base-rate neglect, and sunk-cost fallacy, and through newly developed cognitive sensors that infer attackers' cognitive states from behavioral and network data. Three rounds of human-subject experiments (total n=61) in a simulated small business network demonstrate that these manipulations significantly disrupt attacker performance, reducing mission progress, diverting actions off the true attack path, and increasing detectability. These results demonstrate that cognitive biases can be systematically triggered to degrade the attacker's efficiency and enhance the defender's advantage. GAMBiT establishes a new paradigm in which the attacker's mind becomes part of the battlefield and cognitive manipulation becomes a proactive vector for cyber defense. |
| title | Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis |
| topic | Cryptography and Security Computer Science and Game Theory |
| url | https://arxiv.org/abs/2512.00098 |