Local Epistemic Threats in Knowledge Systems

• Local Epistemic Threats (LETs) are context- and observer-specific vulnerabilities arising from localized gaps or distortions in the acquisition, transfer, or preservation of knowledge.
• They are examined through methods such as uncertainty quantification, adversarial manipulation in LLM reasoning, and privacy breach analyses in local agent deployments.
• Mitigating LETs requires targeted interventions like calibrated uncertainty measures, robust privacy engineering, and strategies to preserve epistemic diversity in complex systems.

Local Epistemic Threats (LETs) are context- and observer-dependent phenomena in which gaps, erosions, or exposures in the acquisition, transfer, or preservation of knowledge arise locally—within the epistemic perspective of a particular agent, system, subcommunity, or device. LETs manifest across domains including quantum theory, machine reasoning, human-LLM epistemology, privacy for local agents, and knowledge diversity in LLMs. They reveal points where knowledge, justification, or sensitive content is at risk of distortion, exploitation, or attenuation due to either local information boundaries, unrecognized uncertainty, infrastructure leakage, or epistemic homogenization.

1. Conceptual Foundations of Local Epistemic Threats

LETs originate from a mismatch or boundary between the epistemic resources available to different agents or components within a broader system. In quantum foundations, LETs denote circumstances where local hidden variables enable observer-specific predictive advantages, but epistemic boundaries prevent consensus or global exploitation (Fankhauser, 2023). In human–LLM interaction, LETs arise when agent-level reflective epistemic standards are subverted by reliable externalist mechanisms, weakening knowledge at the locus of professional or civic agents (Hila, 22 Dec 2025). In local LLM deployments, LETs denote exposures in private prompting or user trait information due to observable local behavior even when higher-level system secrecy is preserved (Jeong et al., 27 Aug 2025). LETs also emerge in model-based uncertainty quantification where uncalibrated epistemic risk in new local state–action regimes exposes the system to potential failure (Marques et al., 12 Sep 2024), and in LLM-mediated knowledge access when local, minority, or regional perspectives are homogenized or erased (Wright et al., 5 Oct 2025).

A defining feature of an LET is its localization—either spatiotemporally (e.g., restricted to a neighborhood in state–action space), structurally (within a subsystem or observer frame), or semantically (to subsets of knowledge/belief states). LETs do not necessarily manifest as global epistemic failures but as latent or actual threats that compromise local robustness, privacy, or diversity.

2. LETs in Quantum Theory and Foundations

In quantum information, LETs are formalized as the possibility for an agent (e.g., Maggie) to possess a local hidden variable (z) that yields a predictive advantage regarding another agent's outcome, exceeding the Born rule (Fankhauser, 2023). However, for this advantage to remain strictly local and non-signalling, there must exist an epistemic boundary: Maggie's advantage cannot be reliably communicated or shared with Alice, as doing so would violate reliable intersubjectivity and ultimately the no-signalling constraint. This boundary guarantees quantum uncertainty’s fundamental status: any attempt to operationalize local predictive advantage universally reintroduces quantum unpredictability.

Retrocausal ψ-epistemic models reinforce the LET perspective: the laboratory quantum state $|\psi_e\rangle$ is epistemic, representing knowledge or belief distribution, whereas the ontic state $|\psi_o\rangle$ is local, directly causally involved, and governs outcomes via local dynamics (de Broglie–Bohm guidance) (Sen, 2018). LETs thus constitute a structural challenge to ψ-ontic views, demonstrating that all observed correlations may be derived locally by treating standard quantum states as epistemic while insulating these inferences from transferable or global exploitation.

3. LETs in Machine Reasoning and LLM Robustness

LETs manifest acutely in reasoning LLMs subjected to adversarial or compromised local context. In the “Compromising Thought” (CPT) scenario, modifying only the last token of a chain-of-thought suffices to hijack the model’s epistemic state, bypassing correct reasoning steps and forcing the adoption of erroneous results (Cui et al., 25 Mar 2025). Formally, for a chain $R = (r_1, \ldots, r_n)$, local perturbation $\delta$ at $r_n$ yields $R' = (r_1, \ldots, \tilde{r}_n)$; this LET overrides the model’s own process adherence, demonstrating that local token manipulations can have greater epistemic impact than entire structural edits. Security vulnerabilities (e.g., “thinking stopped” attacks) further illustrate how a local epistemic threat to the reasoning module can propagate to denial-of-service.

Mitigation efforts—including explicit prompting, forced output prefix control, and symbolic checks—can partially ameliorate LETs, but not eliminate them. The phenomenon is distinct from global model brittleness, as attacks are targeted, content-based, and highly localized at the epistemic interface between reasoning steps.

4. LETs in Uncertainty Quantification and Model-Based Planning

Robust model-based systems distinguish between aleatoric (irreducible stochastic) and epistemic (knowledge-based) uncertainty. LETs in this context are characterized as regions in state–action space where epistemic uncertainty is locally high, posing a critical threat to safe planning, as confidence in model predictions becomes unwarranted in these locations (Marques et al., 12 Sep 2024).

The LUCCa method quantifies LETs by performing local conformal calibration on the model’s predictive distribution. Let $\tilde{f}(x,u)$ be the model’s mean with covariance $\Sigmâ(x,u)$. Collected calibration data yields nonconformity scores $R_i$, which are partitioned into local regions $\mathcal{X}_k$ and used to compute empirical quantiles $\hat{q}_{\alpha,k}$ giving region-specific scaling factors:

$$\xi(x,u) = \hat{q}_\alpha(x,u)^2 / \chi^2_{d,1-\alpha}$$

The calibrated uncertainty region $C_{x,u}^\alpha$ is then an ellipsoid that guarantees

$$P(y_{\text{new}} \in C_{x_{\text{new}},u_{\text{new}}}^\alpha) \geq 1-\alpha$$

in each region, rendering the local epistemic threat explicit and measurable. Embedding these calibrated regions in planning ensures safety against both aleatoric effects and local epistemic threats, as validated empirically with robust performance in OOD and regime-shift scenarios.

5. LETs in Human-LLM Epistemology and Collective Knowledge

LETs in collective intelligence frameworks are diagnosed as a degradation of reflective epistemic practices at the level of individual agents due to excessive reliance on LLMs' externalist reliabilism (Hila, 22 Dec 2025). The distinction is formalized as:

• Internalist Justification (IJ): $J_i(p)=1$ iff agent $i$ has reflective access to the reasons for $p$.
• Externalist Justification (Reliabilism): $$R(S,p) = \Pr[p \textrm{ true} \mid S \textrm{ generates } p] \geq \tau$$.

LETs arise when human agents disproportionately trust LLM outputs without engaging in internalist processes, thus undermining the conditions for genuine knowledge. Consequences include weakening of reflective standards, disincentivization of comprehension, and abdication of professional/civic epistemic duties. Mitigation strategies are structured as a three-tiered program: epistemic interaction models for individuals, institutional norm frameworks, and deontic constraints at organizational/legislative levels—all aimed at restoring the joint necessity of reflective and reliable knowledge.

6. LETs in Local Privacy, Prompt Leakage, and Trait Inference

Locally deployed research and web agents (WRAs) powered by LLMs are subject to LETs when network-level metadata—domain names, timings, and payload sizes—permit external reconstruction of both prompt contents and latent user traits despite cryptographic protection of protocol-level data (Jeong et al., 27 Aug 2025). The OBELS (Ontology-aware Behavioral Leakage Score) metric quantifies how well a behavioral trace reproduced from such metadata matches the original prompt in dimensions of intent, source type, and entity specificity.

Empirically, behavioral fingerprinting of local agent activity allows recovery of 73% of functional and domain prompt information, with up to 19 of 32 latent user traits inferred over multiple sessions, even with partial or noisy observation. LETs here are a privacy breach localized at the boundary between on-device epistemic state and externally observable behavior. Mitigation—via trace hiding with decoy prompts, blocking with source constraints, and network-level anonymization—achieves partial reduction, but the inherent exposure persists unless full network protections are used.

7. LETs and Epistemic Diversity Collapse in LLMs

LETs are instantiated at the population knowledge level by the systematic decline in epistemic diversity—i.e., the variety of real-world claims LLMs can produce for a given topic across prompts and cultural contexts (Wright et al., 5 Oct 2025). Using Hill–Shannon diversity,

$$S(X_{m,t}) = \exp\left(-\sum_{i=1}^K p_i \ln p_i\right)$$

and coverage/rarefaction methods, empirical studies show that, although newer models and retrieval-augmented generation (RAG) can increase diversity, almost all current LLMs exhibit substantially less epistemic diversity than open-web baselines, and large models in particular exacerbate knowledge collapse. LETs here threaten access to minority, regional, or culturally distinct knowledge, especially as model outputs cluster towards global or English-dominated perspectives. RAG and curated retrieval indices are only effective mitigations when their underlying sources are themselves epistemically diverse and representative.

8. Cross-Domain Synthesis and Prospective Directions

LETs are a unifying concept for failures in epistemic robustness, privacy, diversity, and justifiability that arise locally—either by observer, by context, or by subcommunity. They connect challenges in quantum foundations, algorithmic reasoning, human–machine epistemology, agent privacy, and global knowledge organization.

Mitigating LETs requires: (i) local quantification and calibration of uncertainty in high-risk regimes (Marques et al., 12 Sep 2024), (ii) architectural and procedural interventions to restore reflective standards at the agent level (Hila, 22 Dec 2025), (iii) privacy engineering at the device–network boundary (Jeong et al., 27 Aug 2025), and (iv) data and training regime design that maintains representational epistemic diversity (Wright et al., 5 Oct 2025). Across domains, the study of LETs motivates new operational principles for system design, measurement-driven monitoring, and theoretical extensions to identify and control boundary-induced epistemic vulnerabilities.