MaskmyPrompt
ProductFreeAnonymize prompt before sending it to...
Capabilities6 decomposed
pattern-based pii detection and masking
Medium confidenceScans user-provided prompts for common personally identifiable information patterns (names, email addresses, phone numbers, financial account numbers, medical record identifiers) using regex or NLP-based pattern matching, then replaces detected values with anonymized tokens (e.g., [NAME_1], [EMAIL_1]) before transmission to ChatGPT. The system maintains a local mapping table to enable optional de-anonymization of responses post-retrieval, though this mapping is not persisted across sessions by default.
Implements client-side pattern-based PII detection with local token mapping rather than relying on server-side redaction, allowing users to maintain control over sensitive data without transmitting raw PII to any external system. The masking occurs in the browser before ChatGPT API calls, creating a privacy boundary at the point of transmission.
Simpler and faster than manual redaction workflows, but weaker than cryptographic encryption or differential privacy approaches because masking is deterministic and reversible, making it vulnerable to inference attacks if the token mapping is exposed.
one-click prompt anonymization workflow
Medium confidenceProvides a streamlined UI that accepts raw prompts, automatically detects and masks PII in a single action, and forwards the sanitized prompt to ChatGPT without requiring users to manually identify or redact sensitive fields. The workflow includes optional review/edit steps where users can verify masked content before submission, reducing friction compared to manual copy-paste redaction.
Reduces privacy-conscious prompt submission to a single-click action with optional review, eliminating the cognitive load of manual redaction. The design prioritizes accessibility over technical depth, making privacy protection available to non-technical users without requiring regex knowledge or data classification expertise.
More user-friendly than manual redaction or DIY regex-based masking scripts, but less robust than enterprise data loss prevention (DLP) tools because it lacks machine learning-based context understanding and has no organizational policy enforcement.
local session-scoped token mapping and de-anonymization
Medium confidenceMaintains an in-memory mapping table during a browser session that tracks the relationship between original PII values and their anonymized tokens (e.g., {[NAME_1]: 'John Smith', [EMAIL_1]: 'john@example.com'}). After receiving ChatGPT's response, users can optionally trigger de-anonymization to replace tokens back with original values, restoring readability without re-exposing data to OpenAI. The mapping is not persisted across sessions or backed up, requiring users to maintain their own records if long-term reference is needed.
Implements client-side, session-scoped token mapping that allows users to maintain a local reference to original values without persisting sensitive data to any server. This design trades durability for privacy — the mapping exists only in browser memory and is automatically discarded on session end, preventing long-term data leakage through stored mappings.
More privacy-preserving than server-side mapping storage (which could be breached or subpoenaed), but less convenient than persistent de-anonymization because users must manually manage the mapping across sessions or lose the ability to reverse-substitute.
free-tier privacy access without authentication
Medium confidenceOffers core anonymization functionality at no cost and without requiring user registration, login, or API key management. The tool operates entirely client-side in the browser, eliminating the need for backend infrastructure to track users or store session data. This design removes financial and authentication barriers to privacy-conscious AI usage, though it also means no user-specific features, history, or cross-device synchronization.
Eliminates authentication and backend infrastructure entirely, operating as a pure client-side tool that requires no account creation, login, or data transmission to MaskMyPrompt servers. This design choice prioritizes user privacy and accessibility over feature richness and personalization, making privacy protection available to anyone with a browser.
More accessible than enterprise DLP tools or privacy-as-a-service platforms that require registration and backend processing, but less feature-rich because it cannot offer history, cross-device sync, or advanced ML-based detection without server-side infrastructure.
browser-based client-side processing with no server transmission
Medium confidenceExecutes all PII detection, masking, and token mapping logic entirely within the user's browser using JavaScript, ensuring that raw prompts and sensitive data never leave the client device before anonymization. The tool does not transmit prompts, mappings, or metadata to MaskMyPrompt servers — only the anonymized prompt is sent to ChatGPT's API. This architecture eliminates MaskMyPrompt as a potential data intermediary, though it also means no server-side logging, analytics, or advanced ML models.
Implements a zero-trust architecture where all sensitive data processing occurs in the browser, eliminating MaskMyPrompt as a data intermediary entirely. Raw prompts and PII never leave the client device, reducing the attack surface and removing the need for users to trust MaskMyPrompt's data handling practices.
More privacy-preserving than cloud-based privacy services that process data on servers, but less capable because it cannot leverage server-side ML models, centralized threat intelligence, or advanced detection algorithms that require computational resources beyond browser capabilities.
deterministic token-based pii replacement
Medium confidenceReplaces detected PII values with deterministic, human-readable tokens that follow a consistent naming scheme (e.g., [NAME_1], [EMAIL_1], [PHONE_1]) based on the type and order of detection. The same PII value always maps to the same token within a session, enabling consistent reference in multi-turn conversations and allowing users to manually track which token corresponds to which data type. However, the deterministic nature makes the masking structure obvious and potentially vulnerable to inference attacks if an attacker knows the token naming convention.
Uses deterministic, type-labeled tokens ([NAME_1], [EMAIL_1]) instead of random hashes or UUIDs, making the masking structure transparent and human-readable. This design prioritizes usability and consistency over cryptographic security, allowing users to manually verify masking and maintain context across multi-turn conversations.
More transparent and user-friendly than opaque hashing or random token generation, but less secure because the deterministic structure and type labels reveal information about the masked data and make inference attacks easier.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
Related Artifactssharing capabilities
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Presidio
Microsoft's PII detection and anonymization SDK.
Best For
- ✓Healthcare professionals handling HIPAA-regulated patient data
- ✓Legal practitioners discussing confidential client matters
- ✓Finance/compliance teams processing sensitive transaction data
- ✓Developers debugging code containing secrets or credentials
- ✓Non-technical professionals in regulated industries who need privacy but lack data engineering skills
- ✓Teams seeking a low-friction privacy layer without complex configuration
- ✓Organizations wanting to reduce human error in manual data redaction
- ✓Users who need to read responses in context but want to minimize PII exposure to OpenAI
Known Limitations
- ⚠Pattern-based detection has false negatives — context-dependent PII (e.g., 'John' as a person vs. 'john' as a variable name) may not be reliably distinguished
- ⚠No verification that OpenAI's backend systems don't retain or log the anonymized prompts after receipt
- ⚠Masking is irreversible without maintaining the local token-to-value mapping, creating a separate data management burden
- ⚠Effectiveness depends entirely on user correctly identifying what needs masking — no active guidance or validation
- ⚠No support for domain-specific PII patterns (e.g., medical codes, legal case numbers) beyond common formats
- ⚠One-click automation creates a false sense of security — users may assume all sensitive data is masked when patterns are missed
Requirements
Input / Output
UnfragileRank
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About
Anonymize prompt before sending it to ChatGPT.
Unfragile Review
MaskMyPrompt addresses a genuine privacy concern by anonymizing sensitive data in ChatGPT prompts before transmission, making it valuable for professionals handling confidential information. However, the tool's effectiveness depends entirely on users correctly identifying what needs masking, and it offers no verification that OpenAI actually deletes the anonymized data afterward.
Pros
- +Solves a real problem for enterprises and healthcare workers who need HIPAA/GDPR compliance when using ChatGPT
- +Free tier removes financial barriers to privacy-conscious AI usage
- +Simple one-click anonymization workflow reduces friction compared to manual data redaction
Cons
- -No guarantee OpenAI won't retain anonymized prompts—privacy relies on trust rather than technical enforcement
- -Requires manual prompt crafting to work effectively; oversimplifies privacy by suggesting masking alone solves data leakage risks
- -Limited transparency about what anonymization techniques are used or whether they're actually irreversible
Categories
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