CueCrux Auth, Trust & Anti‑Manipulation Plan
(User‑Trust Edition)
Introduction
At CueCrux we want to build search and discovery tools that people trust. This means curating evidence carefully, protecting the community from abusive behaviour and ensuring that our systems remain fair and transparent for everyone. The following plan explains our approach to authentication, evidence handling and manipulation detection in a way that is accessible to the users and stakeholders who rely on the platform every day.
Our Objectives
- Keep abuse out without penalising honest users. We aim to stop fake accounts, coordinated attacks and other forms of manipulation while keeping friction low for real people.
- Make every trust decision clear, appealable and reversible. When we down‑weight content or flag suspicious activity, we explain why and provide a path to challenge those decisions.
- Ensure accuracy and timely corrections. Answers never run ahead of their supporting evidence, and updates propagate quickly when sources change or are retracted.
- Preserve viewpoint neutrality. We do not delete content for ideological reasons. Instead, we weigh evidence quality and provenance so that claims are placed in context.
What Is Not Part of This Plan
While we take a strong stance on trust and integrity, we do not hide or delete viewpoints simply because they are unpopular. We also recognise that not everyone has access to official identity documents, so we rely on progressive friction and behavioural signals rather than mandatory identity verification.
Threats We Guard Against
- Identity and account abuse: Fake or “sockpuppet” accounts, coordinated groups trying to game the system, or account takeovers.
- Evidence and citation manipulation: Misleading citation loops designed to create false consensus; reused quotes presented as original research; fake or predatory sources; selective quoting or outdated copies of sources.
- Attacks on our AI models: Attempts to inject malicious instructions into retrieved content or to poison our models by altering the training data.
- Media provenance risks: Synthetic or manipulated images and videos presented as evidence.
Understanding these threats helps us design safeguards without resorting to blanket bans or censorship.
Guiding Principles
- Proof over opinion. Answers come with receipts: quotes, timestamps and links to original sources. Opinions without verifiable backing carry less weight.
- Verifiability over belief. We prioritise whether claims can be traced to reliable sources rather than whether we “agree” with them.
- Separation of powers. Authentication, evidence storage and audit functions operate independently to reduce single points of failure or bias.
- Active search for counter‑evidence. Our systems actively look for contradictory evidence to ensure balance.
- Never delete knowledge. When information is suspect, we mark it and reduce its influence rather than hiding it entirely. Transparency minimises bias.
Protecting Accounts and Identity
We take a “progressive friction” approach: the more risk attached to an action, the more evidence we require that the account is genuine. At the same time we avoid onerous hurdles for normal use.
- Secure tokens. We use short‑lived access tokens and rotating refresh tokens. Sensitive keys are never stored in your browser.
- Rate limits and behavioural monitoring. We watch for sudden bursts of sign‑ups, unusual login patterns or other anomalies and may introduce additional checks (like a CAPTCHA or SMS confirmation) only when risk is high.
- Abuse graphs. We keep a lightweight graph of devices, IP addresses and accounts (without storing unnecessary personal data) to identify networks of fake accounts. When we detect likely Sybils or coordinated behaviour, we reduce the trust weighting of those accounts rather than banning outright.
Strengthening the Evidence Pipeline
To ensure that our answers are based on reliable evidence and remain up‑to‑date, we have several safeguards:
Selecting the Right Evidence
We use QUORUM (MiSES) (Quorum of Unified Observations and Referenced Underlying Material) to select a MiSES (Minimal Evidence Set) per claim. This avoids over‑reliance on a single venue and encourages diversity.
Recording Provenance
Every quote we serve includes a hash of the snippet, when it was observed, the source domain, licensing information and more. That way anyone can check whether the quote has been altered since we retrieved it.
Seeking Counter‑Evidence
An independent “counterfactual” lane queries opposing sources to see whether a claim is contested. Contradictory evidence does not cause deletion; it is displayed alongside the original claim so that readers can judge.
Handling Retractions and Predatory Sources
We check nightly for article retractions and maintain a reputation index for journals and venues. If a source has been retracted or comes from a venue with a history of predatory practices, we mark it and reduce its weighting. We do not automatically remove such sources but clearly flag them.
Verifying Media Authenticity
Where possible we read digital signatures like C2PA to verify that images and videos have not been tampered with. The absence of such credentials does not invalidate a source; it simply removes a potential “authentic media” boost.
Guarding Against Prompt Injection
Our models strip or contain hidden instructions embedded in HTML comments, alt text or markdown that might try to misdirect the AI. We treat such content as untrusted and seek independent confirmation.
Detecting Manipulation Without Censorship
Trust and reputation on CueCrux are based on a combination of evidence quality and community behaviour. Here is how we handle manipulation attempts:
Collusion & Coordination
We compute a suspicion score for clusters of users and sources based on how tightly they cross‑reference each other, how closely they publish in time and how similar their quotes are. When that score exceeds a threshold, we quarantine the cluster: content remains visible but its influence on rankings is dampened and readers see a “collusion suspected” notice.
Encouraging Better Citations
If a claim cites a secondary summary instead of the primary study, we suggest the primary source. We warn when quoted text doesn’t actually contain the key terms it claims to support or when the source content has changed significantly since it was quoted.
Protecting Against Vote Brigades
Reputation gains from up‑votes diminish if they come from low‑diversity cohorts or from accounts that appear to be coordinated. Sudden spikes in voting trigger audits and temporary damping of the associated reputation gains.
Governance & Fairness
Transparency is at the heart of our system. Every time we reduce an evidence set’s influence or quarantine a cluster, we create a signed ledger entry explaining why. Users can appeal these decisions by submitting counter‑evidence, and any reversals are logged publicly.
We never ban viewpoints based on ideology. Instead we assess the reliability and independence of the sources behind claims. An independent audit service, WatchCrux, runs outside our main pipeline and publishes PASS/WARN/FAIL findings along with artefacts for anyone to review.
Implementation Overview (High‑Level)
Behind the scenes we make a few technical changes to support the above policy. For interested stakeholders:
- We store extra metadata on citations (hashes, timestamps, licence information) and maintain small graphs of user interactions to detect suspicious patterns.
- We run regular batch jobs to sync retraction databases, update venue reputations, detect collusion and verify media credentials.
- We provide read‑only API endpoints so that users can request a trust report for any answer and see how different subscores (domain diversity, recency, authority and contradiction) contribute to the overall trust level.
- A separate appeals endpoint allows users to challenge trust decisions by submitting new evidence.
Measuring Data Health
To keep ourselves accountable, we track metrics such as:
- Contradiction rate: How often new sources contradict our existing answers. A stable or declining rate suggests our evidence selection is robust.
- Citation independence index: The proportion of unique domains in our citations; higher values mean we avoid echo chambers.
- Retracted source utilisation: The number of times retracted or predatory sources appear in answers and how quickly we flag them.
These metrics are visualised by WatchCrux and shared openly with the community. Our Service Level Objectives include targets for keeping contradiction rates low and diversity high.
Conclusion
The goal of this plan is to build a trustworthy, bias‑resistant system that empowers users rather than policing them. Through clear evidence, progressive account verification, active detection of manipulation and transparent governance, we aim to ensure that CueCrux remains a reliable resource for all users and stakeholders.