arXiv subjects

Sofia Hirschmann

Publications and source records attributed to Sofia Hirschmann.

4 recordsLinked to original sources

arXiv2025

Detecting Ambiguity Aversion in Cyberattack Behavior to Inform Cognitive Defense Strategies

Adversaries (hackers) attempting to infiltrate networks frequently face uncertainty in their operational environments. This research explores the ability to model and detect when they exhibit ambiguity aversion, a cognitive bias reflecting a preference for known (versus unknown) probabilities. We introduce a novel methodological framework that (1) leverages rich, multi-modal data from human-subjects red-team experiments, (2) employs a large language model (LLM) pipeline to parse unstructured logs into MITRE ATT&CK-mapped action sequences, and (3) applies a new computational model to infer an attacker's ambiguity aversion level in near-real time. By operationalizing this cognitive trait, our work provides a foundational component for developing adaptive cognitive defense strategies.

cs.CR↗

arXiv2025

Guarding Against Malicious Biased Threats (GAMBiT): Experimental Design of Cognitive Sensors and Triggers with Behavioral Impact Analysis

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.

cs.CR↗

arXiv2025

Risk Psychology & Cyber-Attack Tactics

We examine whether measured cognitive processes predict cyber-attack behavior. We analyzed data that included psychometric scale responses and labeled attack behaviors from cybersecurity professionals who conducted red-team operations against a simulated enterprise network. We employed multilevel mixed-effects Poisson regression with technique counts nested within participants to test whether cognitive processes predicted technique-specific usage. The scales significantly predicted technique use, but effects varied by technique rather than operating uniformly. Neither expertise level nor experimental treatment condition significantly predicted technique patterns, indicating that cognitive processes may be stronger drivers of technique selection than training or experience. These findings demonstrate that individual cognitive differences shape cyber-attack behavior and support the development of psychology-informed defense strategies.

cs.CR↗

arXiv2025

Quantifying Loss Aversion in Cyber Adversaries via LLM Analysis

Understanding and quantifying human cognitive biases from empirical data has long posed a formidable challenge, particularly in cybersecurity, where defending against unknown adversaries is paramount. Traditional cyber defense strategies have largely focused on fortification, while some approaches attempt to anticipate attacker strategies by mapping them to cognitive vulnerabilities, yet they fall short in dynamically interpreting attacks in progress. In recognition of this gap, IARPA's ReSCIND program seeks to infer, defend against, and even exploit attacker cognitive traits. In this paper, we present a novel methodology that leverages large language models (LLMs) to extract quantifiable insights into the cognitive bias of loss aversion from hacker behavior. Our data are collected from an experiment in which hackers were recruited to attack a controlled demonstration network. We process the hacker generated notes using LLMs using it to segment the various actions and correlate the actions to predefined persistence mechanisms used by hackers. By correlating the implementation of these mechanisms with various operational triggers, our analysis provides new insights into how loss aversion manifests in hacker decision-making. The results demonstrate that LLMs can effectively dissect and interpret nuanced behavioral patterns, thereby offering a transformative approach to enhancing cyber defense strategies through real-time, behavior-based analysis.

cs.CR↗