Discover 8 common types of platform abuse — from fake jobs to deepfakes — and learn how to prevent fraud, spam, and identity risks effectively.
Online platforms are the backbone of modern digital life, powering everything from job boards and social networks to marketplaces and fintech apps. But with scale comes risk: where there are users, there will inevitably be abusers. Fraudsters, scammers, and bad actors exploit platforms to commit fraud, spread misinformation, or manipulate systems for profit. If left unchecked, abuse erodes user trust, harms legitimate participants, and damages the platform’s reputation. The good news? With the right strategies and safeguards, you can detect, deter, and prevent platform abuse before it becomes a crisis. In this article, we’ll explore eight of the most common types of platform abuse — and the practical steps you can take to defend against them.
Fraudulent job postings are one of the most damaging forms of abuse, particularly for job boards and hiring platforms. Scammers create fake listings to steal personal information, harvest CVs, or lure applicants into fraudulent schemes that request upfront payments.
How to prevent it: Implement job fraud detection checks. This includes analysing job descriptions for suspicious language, benchmarking salaries against norms, checking domains and company credentials, and running behavioural analysis on the poster’s activity. Fraud detection APIs, like Ruvia’s Trust API, provide automated risk scoring to flag suspicious jobs before they reach applicants.
Once attackers compromise a user account, they can spread spam, commit fraud, or siphon funds. ATOs often happen through weak passwords, credential stuffing, or phishing attacks.
How to prevent it: Enforce multi-factor authentication (MFA), implement rate limiting for login attempts, and monitor unusual login patterns. Device fingerprinting and IP-based anomaly detection can stop many takeover attempts in real time.
Bots are responsible for a significant portion of platform abuse. They can create fake accounts at scale, flood content systems with spam, or manipulate engagement metrics.
How to prevent it: Introduce proof-of-personhood checks that distinguish humans from automated scripts. Behavioural analysis (e.g. typing cadence, navigation flow) is more reliable than CAPTCHAs, which bots increasingly bypass. Rate limiting, velocity checks, and device fingerprinting also help reduce automated abuse.
Marketplaces, gig platforms, and fintech apps are prime targets for payment fraud. Fraudsters may use stolen cards, set up fake sellers, or run refund abuse schemes.
How to prevent it: Integrate fraud prevention tools that check card reputation, device/IP patterns, and behavioural anomalies. Require identity verification for sellers and set withdrawal delays for new accounts to reduce chargeback risks.
On e-commerce, SaaS, and app stores, reviews are powerful. But fake reviews — often generated by bots or paid farms — distort trust and deceive real users. For job platforms, fake employer reviews can mislead candidates into unsafe applications.
How to prevent it: Detect abnormal review activity such as bursts of ratings from new accounts or repetitive language. Combine NLP models to flag suspicious text patterns with user reputation scoring. Requiring verified transactions or identity checks before posting reviews adds another barrier.
Whether it’s AI-generated spam, manipulated media, or outright misinformation, harmful content spreads quickly on platforms that lack proper controls. Deepfake videos, misleading job ads, or false product claims can all cause reputational and legal harm.
How to prevent it: Use AI-powered content moderation to detect misleading or synthetic content. Deepfake detection models, combined with human review for edge cases, help filter harmful uploads. For text, natural language models can spot disinformation patterns or AI-generated spam at scale.
Messaging systems are particularly vulnerable to abuse. Spammers exploit them to send scams, phishing links, or irrelevant promotions. This not only irritates users but also exposes them to risk.
How to prevent it: Rate limit messages for new accounts, filter links through reputation systems, and flag repetitive or bulk-sent content. Behavioural analysis can distinguish between genuine conversations and scripted spam patterns.
Abusers create fake accounts to deceive users, manipulate trust signals, or carry out scams. On job platforms, fake recruiters impersonate legitimate companies. On dating platforms, fake profiles are used for romance scams.
How to prevent it: Require identity verification for high-risk roles such as employers or sellers. Proof-of-personhood scoring, document checks, and biometric verification all reduce the prevalence of fake accounts. Transparent verification badges help users trust who they are dealing with.
Each of these abuse types poses unique challenges, but collectively they have a much bigger impact: the erosion of user trust. Once users believe your platform is unsafe, they stop engaging, churn rises, and growth stalls. Regulators are also increasingly scrutinising platforms for their responsibility in preventing fraud and harmful content. Prevention isn’t just a technical exercise — it’s central to your business model and brand reputation.
The most effective platforms use a layered approach to safety:
Platform abuse isn’t going away. In fact, as AI lowers the barrier for fraudsters, abuse will become more sophisticated. The platforms that thrive will be those that invest early in trust infrastructure, proving to users, regulators, and investors that they take safety seriously. Whether you’re running a job board, marketplace, or SaaS platform, building safeguards against these eight forms of abuse will ensure you protect both your users and your business.
Platform abuse refers to malicious behaviour on digital platforms, such as fake job postings, spam, payment fraud, misinformation, and account takeovers, which undermine user trust and safety.
Fake job postings trick candidates into sharing sensitive data, paying upfront fees, or applying for non-existent roles, exposing them to identity theft and fraud.
Yes, AI models can detect deepfakes and disinformation patterns, though the most reliable systems combine automated detection with human moderation for accuracy.
Preventing spam and fake reviews involves rate limiting, behavioural analysis, link filtering, and requiring verified identities or transactions before reviews are posted.
Proof of personhood helps distinguish real humans from bots or fake accounts, reducing automated abuse, identity fraud, and the creation of fake profiles.
Regulators expect platforms to take proactive steps to prevent fraud, protect users, and comply with data protection laws like GDPR and CCPA, with penalties for negligence.
Common signs of account takeovers include unusual login locations, rapid credential stuffing attempts, device changes, and sudden spikes in suspicious activity.