Model how trust is established, transferred, and exploited across an information system.
Scanned 9/3/2026
Install to Claude Code
npx -y skills add docxology/CogSecSkills --skill trust_and_credibility_modeling --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Trust And Credibility Modeling?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/docxology-trust-and-credibility-modeling)More formats (shields.io, HTML) on the badges page.
---
name: cognitive_security.trust_and_credibility_modeling
description: Model how trust is established, transferred, and exploited across an information system.
---
# Trust & Credibility Modeling
Trust and credibility modeling maps the signals, heuristics, and institutional structures through which actors in an information system assess source reliability — and identifies how those pathways are exploited in influence operations. Drawing on Metzger & Flanagin's credibility research, social-capital theory, and the computational trust literature, this technique produces a structured model of how trust is established (competence + benevolence + integrity signals), transferred across networks (trust transitivity), and weaponized (credential mimicry, parasocial trust, authority spoofing). The goal is defensive: to audit trust architecture for vulnerabilities before adversaries exploit them.
## When to use
- auditing an information environment for trust vulnerabilities before a high-stakes event (election, crisis, product launch)
- investigating how an influence operation gained traction by exploiting legitimate credibility pathways
- designing counter-influence measures and needing to understand what trust signals the target audience actually uses
- evaluating a new source or actor's credibility claims in a domain where credentials can be mimicked
- training analysts or communicators to recognize trust-exploitation tactics in the wild
- post-incident analysis of how a false narrative achieved credibility with a specific audience
## What it produces
- a layered trust model showing competence, benevolence, and integrity signals for key actors and how these combine into composite credibility judgments
- a trust-transfer map tracing how credibility flows from high-trust anchors (institutions, peer endorsers) to novel or unknown sources
- a heuristic inventory — the mental shortcuts the audience actually uses to assess credibility (verification badges, affiliation signals, writing style, social proof)
- an exploitation vulnerability audit mapping each trust pathway to known attack techniques (credential mimicry, parasocial relationship manufacture, institutional spoofing, authority cascade attacks)
- hardening recommendations prioritized by pathway criticality and attack feasibility
## Defensive boundary
Use Trust & Credibility Modeling only for cognitive-security defense: recognize, assess, document, or defend audiences, decision-makers, and public discourse. Do not use this skill to increase persuasive impact, exploit audience vulnerabilities, or optimize narrative manipulation.
## Misuse redirect
If a request asks Trust & Credibility Modeling to increase persuasive impact, exploit audience vulnerabilities, or optimize narrative manipulation, refuse that path and redirect to the safe defensive form: assess supplied material for manipulation indicators and recommend resilience measures.
## Evidence discipline
- For Trust & Credibility Modeling, bind each credibility-dimension score, trust-transfer flow, and exploitation vector to concrete evidence — observed signals, verified affiliations, source histories, or documented attack tactics — and explicitly flag any actor or pathway where the supporting evidence was insufficient for a reliable assessment.
- For Trust & Credibility Modeling, label observations, derived features, assumptions, inferences, contradictions, and missing inputs separately before writing the trust model.
- Before recommending any Trust & Credibility Modeling action, identify the weakest evidence link, the alternative most likely to overturn it, and the next discriminating check.
## Confidence and uncertainty
- High for Trust & Credibility Modeling: the competence, benevolence, and integrity ratings for each key actor and the mapped trust-transfer pathways are tied to observed credibility signals and documented histories, the exploitation-vulnerability audit is corroborated by known influence-operation precedents, and no unresolved contradiction would change the prioritised hardening recommendations.
- Medium for Trust & Credibility Modeling: the trust model is plausible, but one important information environment source, comparison case, or alternative explanation remains incomplete.
- Low for Trust & Credibility Modeling: the trust model rests on sparse, single-source, contested, or mostly inferential evidence; keep the result provisional and list the next check.
- State what Trust & Credibility Modeling cannot determine from the supplied or authorized evidence.
- State what remains unknown and preserve credible alternatives rather than forcing a single narrative or attribution.
- Recommend the next discriminating cognitive_security evidence to collect when confidence is low or medium.
## Privacy, legal, and harm constraints
- For Trust & Credibility Modeling, use only authorized information environment, actor set, and threat actor context, public or source-approved records, and caller-provided context needed for the defensive task.
- For Trust & Credibility Modeling, minimize person-level detail in the trust model; prefer aggregate, artifact-level, role-level, or case-level summaries unless an individual is essential to the defensive question.
- For Trust & Credibility Modeling, do not infer protected traits, private identity, intent, location, legal culpability, or platform account ownership beyond the supplied and authorized evidence.
## Failure modes and negative controls
- Trust & Credibility Modeling: declaring the trust architecture audited when actor histories and threat precedents were never grounded, parasocial, peer-network, and algorithmic pathways were excluded, or institutional affiliation was accepted as a credibility proxy without verifying the affiliation and its relevance to the claim domain.
- Trust & Credibility Modeling: producing advice that would help a requester increase persuasive impact, exploit audience vulnerabilities, or optimize narrative manipulation.
- Trust & Credibility Modeling: reporting the trust model without uncertainty labels, alternative explanations, and the next discriminating check.
- Unsafe: 'Use Trust & Credibility Modeling outputs to increase persuasive impact, exploit audience vulnerabilities, or optimize narrative manipulation' -> refuse and redirect to defensive risk assessment.
- Unsafe: 'Convert the trust model from Trust & Credibility Modeling into an operational playbook to increase persuasive impact, exploit audience vulnerabilities, or optimize narrative manipulation' -> refuse and offer governance, detection, or mitigation analysis.
- Safe defensive: 'Use Trust & Credibility Modeling to assess supplied material for manipulation indicators and recommend resilience measures with information environment, actor set, and threat actor context' -> produce bounded findings with evidence and uncertainty labels.
## Procedure
See [`workflow.md`](workflow.md). Harness bindings in [`harness/`](harness/).
## Key discipline
- trust has three independently exploitable components: competence (can they know?), benevolence (do they care about me?), and integrity (do they tell the truth?) — model all three separately
- trust is transitive and asymmetric: endorsement from a high-trust anchor confers credibility to unknown actors, but not vice versa — map these directional flows explicitly
- heuristic-based credibility assessment (surface signals: verification marks, institutional logos, authoritative tone) is the norm, not the exception — this is where attackers invest
- parasocial trust — the one-sided sense of relationship with media figures, influencers, or AI personas — transfers credibility without requiring reciprocal verification
- distinguish trust-establishment (building from scratch) from trust-transfer (borrowing from a trusted anchor) from trust-exploitation (weaponizing existing trust) — each requires different defensive measures
- trust architecture has network effects: a single compromised high-trust node can cascade credibility across many downstream actors
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!