Context-Aware Safety Assessment

• Context-Aware Safety Assessment is a framework that integrates user vulnerability to quantify safety gaps by contrasting universal and context-specific risk scores.
• Empirical methodologies such as paired evaluations, persona-driven prompting, and regression analyses reveal differential risks across AI, cybersecurity, and vehicular domains.
• Findings suggest that tailored, context-aware strategies can mitigate inherent safety discrepancies, guiding regulatory frameworks and future research.

Safety gap moderated by user vulnerability refers to the phenomenon in which the measured or actual risk associated with a system, process, or output—especially in AI, cybersecurity, or vehicular safety—varies systematically as a function of the end user's or subject’s vulnerability profile. Across domains, the safety gap quantifies the differential between nominal (context-blind or universal) safety assessments and those informed by explicit user vulnerability, with evidence that more vulnerable users systematically incur greater unmitigated risk. This article synthesizes definitions, theoretical bases, empirical methodologies, and salient findings on the moderation of safety gaps by user vulnerability, according to current research on LLMs, cybersecurity, automated vehicles, and cognitive security.

1. Formal Definitions and Theoretical Foundation

The safety gap is consistently formalized as the difference between two safety scores or risk estimates: one derived under a context-blind (universal or average-user) evaluation, and the other from a context-aware evaluation that incorporates detailed user vulnerability information. For LLM safety in user-welfare contexts, the gap is defined as (Kempermann et al., 11 Dec 2025):

$$\Delta(u) = S_{\mathrm{ca}}(u) - S_{\mathrm{cb}}(u)$$

where $S_{\mathrm{ca}}(u)$ is the safety score with user context $u$ (context-aware) and $S_{\mathrm{cb}}(u)$ is the score without (context-blind). $\Delta(u)$ typically becomes negative for high-vulnerability users, and $|\Delta|$ measures the extent to which ignoring vulnerability hides risk.

In code generation, the safety gap is:

$$\Delta_{\mathrm{safety}} = P(Y = 1 \mid \mathrm{Persona} = \mathrm{student}) - P(Y = 1 \mid \mathrm{Persona} = \mathrm{professional})$$

with $Y$ as the incidence of vulnerability generation and persona serving as a proxy for user vulnerability (Bosnak et al., 14 Jul 2025).

Human vulnerability in cybersecurity is conceptualized as a multidimensional profile:

$V = (v_1, v_2, \ldots, v_D)^\mathsf{T}$

where $v_i$ are standardized scores on personality, cognitive, or contextual dimensions. The gap as a function of vulnerability $V$ is modeled as (Papatsaroucha et al., 2021):

$G(V) = [S^* - S_0](1 + \alpha V^p)$

where $S^*$ is the ideal score, $S_0$ is the baseline, and $\alpha, p$ are parameters reflecting the moderation effect.

User vulnerability is operationalized via:

• Demographic/contextual profiles (health, finance, support, literacy) (Kempermann et al., 11 Dec 2025)
• Risk scenarios (illegal/unethical, mental health, physical health) (In et al., 20 Feb 2025)
• Personas (student, professional, etc.) (Bosnak et al., 14 Jul 2025)

2. Methodological Approaches to Assessing Safety Gap Moderation

Empirical analysis of the moderation effect involves experimental or benchmarking protocols that systematically vary user profiles across vulnerability strata. Prominent methodologies include:

• Paired Context-Blind and Context-Aware Evaluation: For LLM advice (finance, health), safety of identical responses is rated both with and without full user profiles. Rich 14-factor context is constructed by professionals, and quantitative differences are measured using ordinal scales and Wilcoxon signed-rank tests (Kempermann et al., 11 Dec 2025).
• Persona-Driven Prompting and Regression: In code synthesis, dynamic prompting assigns explicit user personas to assess differential safety filter effectiveness. Logistic regression and two-way ANOVA quantify moderation by user role (Bosnak et al., 14 Jul 2025).
• U-SafeBench: Benchmarking LLM user-specific safety on curated harmful instructions tied to specific real-world vulnerability profiles, with binary refuse/fulfill outcomes, stratified by risk scenario (In et al., 20 Feb 2025).
• Cognitive Security RCT + LLM Comparison: Human participants and models undergo equivalent interventions (e.g., Think First, Verify Always micro-lesson), allowing direct quantification of the gap across cognitive vulnerabilities (Aydin, 9 Aug 2025).
• Risk Factor (RF) in CAV–VRU Interactions: Risk for vulnerable road users (VRUs) is estimated by incorporating demographic- or mobility-based vulnerability multipliers into real-time risk models (Xhoxhi et al., 23 Apr 2024).

Moderating effects are detected via interaction terms in regression, discrete comparisons across strata, and empirical deltas in performance, with effect sizes and confidence intervals reported.

3. Empirical Evidence and Quantitative Findings

Research consistently demonstrates that user vulnerability sharply moderates the safety gap:

• LLM Advice Safety (Kempermann et al., 11 Dec 2025): For high-vulnerability profiles, context-blind scoring yields "Safe" (∼5/7), whereas context-aware drops to "Somewhat Unsafe" (∼3/7); $\Delta≈-2.0$ ($p < 10^{-13}$). In low-vulnerability cases, context-aware is equal or even higher ($\Delta≈+0.3$ to $+0.7$).
• User-Specific Safety in LLMs (In et al., 20 Feb 2025):

Risk Scenario	Avg. Safety (S)
Illegal/Unethical	42.7%
Mental Health Risk	16.7%
Physical Health Risk	10.3%

Safety gap between lowest and highest vulnerability is 32.4 pp, overwhelming inter-model SD ($\sim$10 pp).

• Vulnerability in LLM Code Generation (Bosnak et al., 14 Jul 2025): Persona explains 15–20 pp of variance ($\eta^2\approx0.18$). Student persona yields up to 5 pp higher vulnerable output rates (e.g., Student vs. Software Engineer: $\Delta=3.7$, $p=0.02$).
• Cognitive Security (Aydin, 9 Aug 2025): Humans show consistent mitigation (+7.9 pp post-intervention), but LLMs display vulnerability- and architecture-specific resistance or backfire, with model–human gap $G_v^M$ largest for context integration and source-memorization failure.
• VRU Risk (Xhoxhi et al., 23 Apr 2024): High-vulnerability users (e.g., elderly) have amplified RF (Risk Factor), with illustrative $w_A=1.3$ boosting median risk by 30%, and CAV-enabling only reduces median RF for these users from $0.79\to0.44$.

4. Operationalization of Vulnerability and Stratification Schemes

Frameworks and studies consistently stress multidimensional operationalization:

• LLM Advice/Benchmarks: Vulnerability profiles partitioned into low/medium/high strata using combinations of 14 demographic/contextual factors—financial fragility, health, support, resource access (Kempermann et al., 11 Dec 2025). U-SafeBench uses 157 profiles spanning medical and criminal backgrounds across three risk scenarios (In et al., 20 Feb 2025).
• Cybersecurity: Vulnerability vector $V$ as weighted sum of personality, cognitive, behavioral, and environmental factors. Empirical weights ($w_i$) may derive from regression or expert judgement (Papatsaroucha et al., 2021).
• Cognitive Security: Each cognitive vulnerability (CCS-7) considered an axis; moderator effect measured as the human–model mitigation delta (Aydin, 9 Aug 2025).
• Road Safety: VRU class (e.g., pedestrian, cyclist, elderly) modulates risk weights/multipliers in real-time models (Xhoxhi et al., 23 Apr 2024).

These designs enable quantification of safety gap modulation across heterogeneity in user characteristics.

5. Limits of Naive Context Enrichment and Mitigation Strategies

Empirical results establish that simple enrichment of prompts or evaluation with partial context fails to eliminate the vulnerability-moderated safety gap:

• Prompt Enrichment: Adding 1–5 key user-context factors to the prompt (drawn by professional relevance or user self-disclosure likelihood) narrows but does not close the gap; at level 5, high-VPL gap reduces from ∼1.9 to ∼1.3, never eliminated (Kempermann et al., 11 Dec 2025).
• Chain-of-Thought Remedies: Explicit two-step reasoning about user profile (inferring “do-not-answer” guidelines) increases average safety by 6.7 pp (from 21.3% → 28.0%) but does not eliminate the 30+ pp scenario gap (In et al., 20 Feb 2025).
• Cognitive Security Guardrails: Prompt-based interventions (e.g., TFVA) partially mitigate some vulnerabilities but cause backfire or limited effect in others, especially when model verification ability is absent (Aydin, 9 Aug 2025).
• Road Safety Mitigation: Increasing sensor coverage or awareness ratios (EAR) yields modest gains compared to targeted, vulnerability-aware parameter tuning or infrastructure in hotspot areas (Xhoxhi et al., 23 Apr 2024).

The qualitative analysis supports that only fully holistic user-context (rather than key-factor or self-disclosure alone) enables accurate risk detection for vulnerable users.

6. Frameworks, Regulatory Implications, and Future Research Directions

Research highlights that universal-risk frameworks are insufficient where vulnerability stratification is necessary:

• Evaluation and Regulation: Frameworks such as OECD's and EU DSA Article 34/40 are expected to require vulnerability-stratified, context-aware safety evaluations for LLM and AI systems (Kempermann et al., 11 Dec 2025).
• Benchmarking and Governance: Introduction of U-SafeBench and similar instruments supports standardized measurement of user-specific safety, but further validation against expert annotation and memory-accumulated context is needed (In et al., 20 Feb 2025).
• Model Development: Post-training or RLHF schemes that explicitly condition on user vulnerability, or infer vulnerability from rich interaction histories, are open research directions.
• Application to Non-AI Domains: Safety gap moderation is also critical in cybersecurity human-factors and in physical safety systems such as CAV-VRU risk analysis (Papatsaroucha et al., 2021, Xhoxhi et al., 23 Apr 2024).

Open questions include modeling vulnerability as a continuous score, adaptive refusal/harm thresholds, and adversarial exploitation of vulnerability information.

7. Representative Safety Gap Moderation Patterns Across Domains

The following table summarizes domain-specific safety gap patterns and moderation modalities:

Domain	Moderation Variable	Max Observed Gap
LLM Advice	Demographic/contextual profile	Δ≈–2.0 points/7 (Kempermann et al., 11 Dec 2025)
LLM User-Specific	Risk scenario (health/ethics)	30+ pp (In et al., 20 Feb 2025)
Code Generation	User persona (student vs pro)	3.7–5 pp (Bosnak et al., 14 Jul 2025)
Cognitive Security	Security vulnerability type	Up to 100% (Aydin, 9 Aug 2025)
Road Safety	VRU class (elderly, etc.)	ΔRF=0.35 (Xhoxhi et al., 23 Apr 2024)

These findings demonstrate the systematic, quantifiable effects of user vulnerability on residual risk and model performance, substantiating the necessity of vulnerability-aware design and evaluation in safety-critical systems.