Deceptive UX Design

5 automated security scanners

Purpose: The Login Flow Deception Scanner is designed to identify and mitigate potential vulnerabilities in login processes that could lead to unauthorized access and data breaches. By detecting credential harvesting, social engineering elements, MFA bypass tricks, and other security flaws, this scanner helps organizations safeguard their sensitive information and maintain a secure digital environment.

What It Detects:

Credential Harvesting Indicators: The presence of hidden form fields for additional credentials, phishing-like prompts requesting sensitive information, and analysis of redirects during login processes.
Social Engineering Elements: Identification of suspicious language or prompts that manipulate user behavior, deceptive UI elements designed to trick users into providing credentials, and misleading error messages or instructions on login pages.
MFA Bypass Tricks: Analysis for bypass mechanisms in multi-factor authentication (MFA), detection of weak or easily guessable second factors, and identification of suspicious redirects after MFA prompts.
Security Header Vulnerabilities: Examination of HTTP security headers for weaknesses such as missing Strict-Transport-Security, improperly configured Content-Security-Policy, X-Frame-Options, and X-Content-Type-Options.
TLS/SSL Configuration Issues: Inspection of SSL/TLS configurations for outdated protocols, weak cipher suites, deprecated TLS versions like TLSv1.0 and TLSv1.1, and insecure ciphers including RC4, DES, and MD5.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
url (string): Specific URL of the login page to scan (e.g., https://acme.com/login)

Business Impact: This scanner is crucial for maintaining the security and integrity of login processes, which are critical touchpoints in any digital system where unauthorized access can lead to significant data breaches and potential financial losses.

Risk Levels:

Critical: Conditions that directly compromise authentication mechanisms or result in immediate exposure of sensitive information without multi-factor protection.
High: Conditions that pose a high risk of credential theft through social engineering techniques, such as phishing, without requiring complex technical knowledge to exploit.
Medium: Conditions that may lead to the unauthorized access of systems if not properly secured, potentially compromising user data but with some level of multi-factor protection or complexity required for exploitation.
Low: Informal security issues that do not significantly impact system integrity but are still recommended to be addressed for overall enhanced security posture.
Info: Non-critical findings providing general information about the login process, which does not directly affect security but can be improved for better user experience or compliance with standards.

Example Findings:

A login page includes hidden fields that harvest credentials upon submission.
The domain uses TLSv1.0 and RC4 ciphers, posing significant risks to data security in transit.

Unsubscribe

Purpose: The Unsubscribe Scanner is designed to identify potential obstacles that may hinder users in deleting their accounts and managing their communication preferences on various websites. It aims to ensure transparency and ease of use when it comes to subscription management and data privacy, providing a user-friendly experience for account closure and opting out of communications.

What It Detects:

Account Deletion Obstacles: Hidden or difficult-to-find links for deleting accounts, complex confirmation steps that may mislead users, and lack of clear instructions regarding the deletion process.
Communication Opt-Out Hiding: Subscription management pages without visible options to opt out of communications, multiple clicks required to access unsubscribe features, and use of ambiguous language that does not clearly indicate opting out.
Security Headers Analysis: Inadequate or missing security headers such as Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, and X-Content-Type-Options.
TLS/SSL Inspection: Utilization of outdated TLS protocols like TLSv1.0, TLSv1.1, and the use of weak cipher suites including RC4, DES, MD5.
DNS Record Analysis: Overly permissive SPF records, DMARC policies that do not enforce strict actions (p=none, p=quarantine, p=reject), and absence of DKIM records.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
url (string): Specific URL to scan for account deletion and opt-out options (e.g., https://acme.com/settings)

Business Impact: This scanner is crucial as it directly impacts user control over their personal data, privacy rights, and the overall trustworthiness of websites in managing digital identities securely. Detecting hidden obstacles to account deletion and communication preferences helps users make informed decisions about where they share their information online, ensuring a safer internet environment for all users.

Risk Levels:

Critical: Conditions that lead to severe security vulnerabilities or direct exposure of sensitive data without adequate protection.
High: Conditions that significantly increase the risk of unauthorized access or data breaches, potentially leading to substantial impacts on business operations and reputation.
Medium: Conditions that pose a moderate risk of negative outcomes, such as increased vulnerability to attacks but not critical for immediate action.
Low: Informal findings that do not directly affect security but may indicate areas for improvement in user experience or operational efficiency.
Info: Non-critical issues providing supplementary information about the system’s configuration and potential enhancements without significant direct risk.

If specific risk levels are not detailed in the README, it can be inferred that critical risks relate to severe vulnerabilities, high risks involve substantial data exposure, medium risks indicate moderate threats requiring attention, low risks pertain to minor issues, and informational findings highlight areas for improvement.

Example Findings:

A website may have a hidden account deletion link under complex navigation steps, making it difficult for users to find the option.
An e-commerce site uses outdated TLS protocols and weak cipher suites, increasing the risk of data interception during transmission.

Privacy Control Obfuscation

Purpose: The Privacy Control Obfuscation Scanner is designed to identify hidden settings, confusing toggles, and multi-step barriers that obscure user privacy controls, potentially leading to misuse of user data without clear consent. This tool aims to help users understand how their personal information is being handled on various platforms and take appropriate action to protect their privacy.

What It Detects:

Hidden Settings Detection: Identify settings pages with elements hidden via CSS or JavaScript. Detect settings that are not easily accessible from the main interface.
Confusing Toggles Identification: Analyze toggle switches for ambiguous labels or misleading descriptions. Check for toggles that do not clearly indicate their function (e.g., “Enable Feature” without specifying what feature).
Multi-step Barriers Analysis: Identify processes requiring multiple steps to access or modify privacy settings. Detect unnecessary complexity in the user interface that could deter users from adjusting their privacy preferences.
Security Header Evaluation: Examine HTTP security headers for completeness and best practices, ensuring the presence of critical headers like Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, and X-Content-Type-Options.
TLS/SSL Configuration Inspection: Inspect SSL/TLS configurations to ensure they meet modern security standards. Detect outdated protocols (e.g., TLSv1.0, TLSv1.1) and weak cipher suites (e.g., RC4, DES, MD5).

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com)
url (string): Specific URL to scan for privacy controls (e.g., https://acme.com/privacy)

Business Impact: This scanner is crucial as it helps users navigate complex digital environments and ensure that their personal data is handled with transparency and consent. It contributes to a higher level of user trust in the platforms they interact with, which is essential for maintaining a secure and ethical online presence.

Risk Levels:

Critical: The absence of critical security headers or use of outdated TLS versions and weak cipher suites poses a significant risk to data integrity and confidentiality.
High: Unclear privacy settings or multi-step barriers can lead to user frustration and potential misuse of personal information.
Medium: Somewhat ambiguous toggle labels could confuse users, though the primary impact is on usability rather than security.
Low: Informal findings may include minor UI complexities that do not significantly affect user experience or data protection.
Info: General informational outputs provide baseline details about the configuration of the scanned domain.

Example Findings:

A settings page contains multiple hidden form fields, making it difficult for users to adjust their privacy preferences without technical knowledge.
A toggle labeled “Enable Ads” is unclear and could be mistaken as a security feature rather than a marketing preference setting.

Payment Path Manipulation

Purpose: The Payment Path Manipulation Scanner is designed to identify and report deceptive practices in payment paths that can mislead users into making unwanted payments or subscriptions. It aims to protect consumers from hidden fees, misleading subscription terms, and obstacles to cancellation by analyzing various aspects of a website’s security and checkout processes.

What It Detects:

Fee Hiding: Identifies hidden fees not prominently displayed during the checkout process, which can lead to unexpected charges after initial commitment.
Subscription Tricks: Detects deceptive subscription practices such as auto-renewal without clear opt-out options and ensures upfront communication of all subscription costs and conditions.
Cancellation Obstacles: Uncovers barriers to easy cancellation, including complex processes or hidden fees for early termination.
Security Headers Analysis: Examines HTTP security headers like Strict-Transport-Security, Content-Security-Policy, X-Frame-Options, and X-Content-Type-Options to ensure they are properly configured, which is crucial for protecting user data during transactions.
TLS/SSL Inspection: Inspects SSL/TLS certificates for outdated protocols (e.g., TLSv1.0, TLSv1.1) and weak cipher suites (e.g., RC4, DES, MD5), ensuring the use of secure encryption standards to protect user data during transactions.

Inputs Required:

domain (string): Primary domain to analyze (e.g., acme.com). This is essential for performing comprehensive scans across multiple aspects of the website’s infrastructure and checkout processes.
url (string): Specific URL to scan for payment path manipulations (e.g., https://acme.com/checkout). This allows focused analysis on specific pages that are critical in the user journey related to payments or subscriptions.

Business Impact: Detecting and reporting deceptive practices in payment paths is crucial as it directly impacts consumer trust, financial security, and overall customer satisfaction. It helps organizations avoid legal liabilities and maintain ethical business practices by ensuring transparent and fair transactions for users.

Risk Levels:

Critical: Conditions where hidden fees are not prominently disclosed during the checkout process or when subscription terms are unclear without clear opt-out options.
High: When security headers such as Strict-Transport-Security are missing or improperly configured, and in cases of outdated TLS protocols or weak cipher suites used in SSL/TLS certificates.
Medium: Conditions where cancellation processes are complex or involve hidden fees for early termination.
Low: Informal issues related to the presentation of subscription costs and conditions that do not significantly impact user decision-making but still need attention for continuous improvement.
Info: General compliance with security practices such as using secure headers and protocols, which while not critical, contribute positively to a robust security posture.

Example Findings:

A website may have hidden fees on its checkout page that are only revealed after the user commits to purchase, leading to unexpected charges post-transaction.
An e-commerce site uses outdated TLS protocol versions and weak cipher suites, posing potential vulnerabilities in data protection during transactions.

Forced Opt-in Manipulation

Purpose: The Forced Opt-in Manipulation Scanner is designed to detect deceptive user experience design practices such as hidden consent mechanisms, pre-checked opt-in boxes, and misdirection techniques. This tool aims to ensure users are fully informed about data collection practices and provide explicit consent without being misled or coerced into accepting unwanted services.

What It Detects:

Hidden Consent Mechanisms: The scanner identifies consent forms with hidden fields or default selections that obscure the true nature of data collection, as well as checkboxes that are pre-checked by default without clear user action required.
Pre-checked Opt-in Boxes: The tool scans web pages for checkboxes related to data sharing, marketing communications, or other opt-ins and ensures users must explicitly check the box to opt in rather than being opted in by default.
Misdirection Techniques: It identifies misleading language or design elements that distract users from understanding the implications of their actions, including use of small font sizes, low contrast colors, or other visual cues that make it difficult for users to notice consent prompts.

Inputs Required:

domain (string): The primary domain to analyze (e.g., acme.com)
url (string): Specific URL to scan for deceptive UX design practices (e.g., https://acme.com/subscribe)

Business Impact: This scanner is crucial as it helps in maintaining transparency and user consent in digital platforms, which is essential for building trust and complying with data protection regulations such as GDPR and CCPA. Misleading users into accepting unwanted services can lead to legal repercussions and damage the reputation of the organization.

Risk Levels:

Critical: The scanner flags conditions where hidden consent mechanisms are present or pre-checked opt-in boxes without explicit user action, which could lead to severe privacy and security risks.
High: Misdirection techniques that obscure data collection practices can significantly impact user trust and compliance with data handling policies.
Medium: Issues related to DNS misconfigurations and outdated TLS versions may not directly cause deception but contribute to overall security posture and reliability concerns.
Low: Informational findings regarding missing specific security headers might indicate a minor issue, though they are still relevant for maintaining secure communication protocols.
Info: The scanner’s technical implementation checks like DNS record resolution and TLS configuration ensure the platform is resilient against common attacks that could exploit deceptive practices.

Example Findings:

A website has a hidden consent field named “consent” which is pre-filled without user interaction, potentially misleading users about data collection.
An opt-in checkbox for marketing communications defaults to checked upon loading the page, requiring users to uncheck it instead of simply checking it by default.