Cognitive Hacking (COG)

• Cognitive hacking is the deliberate exploitation of reasoning-level vulnerabilities in both human and AI systems.
• It targets multiple cognitive stages—perception, attention, memory, and decision-making—to induce biases and alter actions.
• Research in COG develops formal models and empirical defenses that quantify attack effects and guide architecture-aware mitigations.

Cognitive hacking (COG) encompasses a spectrum of offensive and defensive techniques that manipulate, exploit, or compromise the reasoning-level functions of human or artificial cognition to achieve adversarial objectives. Whereas traditional attacks focus on technical vulnerabilities or input-output manipulation, COG systematically targets the intermediate cognitive (or reasoning) states—be they step-sequence in AI models, cognitive biases in human agents, or hybrid cyber-cognitive workflows. COG is now recognized in both the AI and cybersecurity domains as an emergent, model- and architecture-dependent threat surface, necessitating new taxonomies, quantitative models, and defense paradigms (Zhao et al., 8 Apr 2025, Aydin, 9 Aug 2025, Beltz et al., 27 Nov 2025, Huang et al., 2024, Huang et al., 2023, Yang et al., 5 Sep 2025).

1. Foundational Concepts and Taxonomies

COG is defined as the deliberate exploitation of reasoning-level vulnerabilities—systematic flaws in how cognitive agents (human or artificial) process, integrate, and act on information (Aydin, 9 Aug 2025). In human-centric systems, these vulnerabilities arise from perceptual limits, attentional bottlenecks, memory constraints, and a panoply of well-documented cognitive biases (confirmation bias, loss aversion, base-rate neglect, sunk-cost fallacy, etc.) (Beltz et al., 27 Nov 2025, Huang et al., 2024, Huang et al., 2023). In AI, particularly LLMs, analogous vulnerabilities are observed as internal reasoning drift, authority hallucination, context poisoning, goal misalignment, and more—summarized in the CCS-7 taxonomy, which systematizes seven key classes (see Table 1).

CCS-7 Vulnerability	Human Analogue	Measurement Metric
Authority Hallucination	Confabulation	DOI fraction of real citations
Context Poisoning	Anchoring/belief revision	Stance slope/net sentiment over turns
Goal Misalignment Loops	Satisficing	Deviation from optimal (task-defined) goals
Identity/Role Confusion	Role-adoption effects	Role disclaimer/failure rates
Memory/Source Interference	False-memory incorporation	Reuse rate of injected misinformation
Cognitive-Load Overflow	Performance under load	Action density, readability, task-to-filler ratio
Attention Hijacking	Emotional override	Emotional-word freq. shift in output

COG unifies these lines: it is not limited to input-output manipulation but targets the stochastic mapping from cognitive state (Hₜ) and attacker action (Aₜ) to future cognitive/behavioral state (Hₜ₊₁) (Huang et al., 2023).

2. Attack Surfaces and Mechanisms

COG attack surfaces map directly onto core stages of cognition:

• Perception: Manipulation of sensory inputs (e.g., phishing that exploits visual priming or sensory overload) (Huang et al., 2023, Rodriguez et al., 2020).
• Attention: Attacks leveraging inattention, task overload, or attentional blink (e.g., feint flooding) (Huang et al., 2023).
• Memory: Techniques inducing false memory, exploiting password reuse, or leveraging the prevalence paradox (Rodriguez et al., 2020).
• Reasoning/Decision-Making: Hijacking inductive/deductive chains, triggering biases (confirmation, sunk-cost, loss aversion) using cognitive triggers (Beltz et al., 27 Nov 2025, Yang et al., 5 Sep 2025, Huang et al., 2024).
• Action: Orchestrating overt behaviors via social engineering or contextually-induced policy shifts.

Modern AI systems introduce a new class: machine reasoning trajectory hijacking. ShadowCoT demonstrates that by perturbing selected attention heads and residual streams, an adversary can inject a stealthy, internally-coherent but logically adverse chain-of-thought (CoT) that subverts downstream task fidelity without detectable anomalies at the surface level (Zhao et al., 8 Apr 2025).

In human-targeted COG, GAMBiT and PsybORG+ operationalize triggers that activate behavioral signatures of specified biases—for example, inserting decoy credential files to induce confirmation bias, or salient false admin accounts to elicit base-rate neglect. Behavioral metrics include off-path action rates, wasted time per event, and increased IDS detectability (Beltz et al., 27 Nov 2025, Huang et al., 2024).

3. Quantitative and Formal Models

COG research leverages formal frameworks across domains:

• Decision-theoretic models: Prospect theory formalizes loss aversion through asymmetric utility functions ε(ω,λ_l), with choice probabilities γ(a,s,λ_l) derived from logit sensitivity to gains/losses (Huang et al., 2024, Beltz et al., 27 Nov 2025).
• Bayesian and biased learning operators: Confirmation bias is modeled as convex combinations of prior and Bayesian-updated beliefs (b_{cb}^{k+1}(θ) = λ * b_{cb}^k(θ) + (1–λ) * b^{k+1}_{\rm Bayes}(θ)), and base-rate neglect as altered weighting in kernel-based inference (Yang et al., 5 Sep 2025).
• Reinforcement learning and Markov games: COG defenses/testbeds simulate attacker-defender games, incorporating belief-update dynamics, cognitive switching budgets, and reward shaping; see the bi-level cyber warfare equilibrium and timing of deceptive strategies (≥ 40% improvement in defender rewards versus no-switch baselines) (Yang et al., 5 Sep 2025).
• HMM/ML-based classification: Cognitive-bias inference from audit logs (PsybORG+) achieves >0.83 accuracy for sunk-cost fallacy states and >0.96 for loss aversion and confirmation bias using Bayesian inference and decision-tree classifiers (Huang et al., 2024).
• LLM-specific pipelines: ShadowCoT injects backdoors via a multistage schema (attention-head localization, supervised/RL tuning, reasoning chain pollution), with corrupted reasoning state updates $s’_t = s_t + \delta_t$ where $\delta_t = f_\theta(s_{t-1}, x_t)$ (Zhao et al., 8 Apr 2025).

4. Representative Frameworks and Empirical Results

• ShadowCoT (LLM Reasoning Backdoor): Achieves Attack Success Rate (ASR) up to 94.4% (AQUA-RAT on Mistral-7B), average Hijacking Success Rate (HSR) ~88.4%, with benign-task accuracy drop <0.3%. Adversarial CoT perplexity is 24.7 (lower and more plausible than prior chain attacks) (Zhao et al., 8 Apr 2025).
• CCS-7 (LLM Cognitive Taxonomy): Identity Confusion can be almost fully mitigated (η>0.9), but mitigation against source interference and attention hijacking backfires in certain architectures (η<0), e.g., source interference error rate increases from 30.3% to 71.2% in Mistral (η=–1.35), demonstrating architecture-dependent risk (Aydin, 9 Aug 2025).
• GAMBiT (Human-Targeted Cognitive Deception): Cognitive triggers reduce mission progress, increase off-path action diversion (diversion rates F(1,35)=10.37, p=0.003), and elevate IDS detectability (t=2.25, p=0.0381). Loss-aversion and base-rate neglect triggers yield mean event times of 30–40 minutes, with statistically significant behavioral and detection impact (Beltz et al., 27 Nov 2025).
• PsybORG+: Synthetic simulation of APT attacker bias states, with Bayesian and tree-based classifiers for confirmed bias inference, and network parameter distributions for varied attacker cognitive profiles (Huang et al., 2024).
• Bi-Level Game-Theoretic Deception: Demonstrates attacker biases such as confirmation and base-rate neglect can be leveraged to amplify defender advantage beyond full-Bayes updating, with further gains from optimal deception timing and mode switching (Yang et al., 5 Sep 2025).

5. Defense, Detection, and Guardrail Engineering

COG detection and defense diverge sharply from conventional perimeter or anomaly-based methods:

• Surface-defenses fail: In LLMs, state-of-the-art detectors (prompt consistency, token-level anomaly checks) are bypassed as COG perturbations fall inside activation bounds and maintain plausible chain-of-thought structure (Zhao et al., 8 Apr 2025).
• Cognitive penetration testing and CPT: CCS-7 advocates pre-deployment multi-condition CPT (control/attack/mitigated), with risk-specific mitigation rates η_v^M to flag backfire (η<0) before release. Positive rates above η>0.2 are required for safe deployment (Aydin, 9 Aug 2025).
• Guardrail principles: Effective interventions must match model capabilities, avoid conflicting goals, emphasize specificity (e.g., role-state displays vs. fact verification), and undergo iterative, architecture-aware testing. TFVA (Think First, Verify Always) micro-lessons yield a +7.9% gain in human cognitive security, but efficacy is model-dependent in AI (Aydin, 9 Aug 2025).
• System-scientific approaches: Defensive paradigms now integrate biosensors, adaptive human-machine interfaces, and RL/Bayesian schedulers to optimize cognitive load and allocate attentional aid (Huang et al., 2023).

A summary of key defense strategies:

• Monitor fine-grained internal reasoning trajectories and compare against clean baselines.
• Employ randomized attention-head masking to disrupt localized hijack.
• Adversarial training with reasoning-level consistency/explication checks.
• Certified robust protocols bounding the influence of parameter subsets (e.g., Lipschitz constraints).

6. Research Challenges and Future Directions

COG presents complex, architecture-specific, and dynamically evolving threats. Outstanding research questions include:

• Theory of reasoning subspace vulnerabilities: Understanding which latent spaces are susceptible to reasoning chain hijack and the mechanics of semantic drift under adversarial manipulation (Zhao et al., 8 Apr 2025).
• Real-time introspection tools: Development of step-wise semantic validators and activation-trajectory monitors for both human and LLM agents (Aydin, 9 Aug 2025).
• Benchmark standardization: Testbeds stressing logic integrity of both AI-generated CoT (e.g., adversarial reasoning paths) and human red-team scenarios (e.g., correlating cognitive triggers with behavioral signatures) (Beltz et al., 27 Nov 2025).
• Ethical and policy risks: As COG defense incorporates persuasion-based (potentially manipulative) techniques, issues of transparency, oversight, and consent enter the picture. Offensive cognitive manipulation may also escalate arms races as adversaries adapt or inoculate their agents (Beltz et al., 27 Nov 2025).
• Integrated human–cyber–AI modeling: COG in hybrid systems (HCPSs) necessitates modular, multi-scale, and transferable models linking human perception and bias with cyber-physical state and adaptive AI controls; open questions remain in the formal synthesis of such architectures (Huang et al., 2023, Yang et al., 5 Sep 2025).
• Parameter inference under partial observability: Bayesian and ML approaches to inferring attacker (or model) bias states from incomplete audit, log, or semantic traces are still being advanced (Huang et al., 2024, Beltz et al., 27 Nov 2025).

7. Significance and Interdisciplinary Implications

COG epitomizes the convergence of fields: adversarial ML, cognitive psychology, behavioral economics, cybersecurity, and human-machine interaction. Its recent formalizations demonstrate that both human and AI agents share systematic cognitive vulnerabilities, the exploitation and defense of which must be quantitatively modeled at both tactical and strategic levels. COG shifts defense paradigms from static, technical perimeter assurance to adaptive, introspective, and behaviorally-targeted methodologies. A plausible implication is that future critical-system security will require continuous, architecture-aware cognitive penetration testing and the deployment of automated, bias-aware defensive mechanisms attuned to the evolving cognitive attack landscape (Zhao et al., 8 Apr 2025, Aydin, 9 Aug 2025, Beltz et al., 27 Nov 2025, Huang et al., 2023).