Browser Integrated Prompt Capture And Injection

via “prompt injection detection via multiple pattern and semantic approaches”

Open-source LLM input/output security scanner toolkit.

Unique: Combines regex pattern matching for known injection signatures with semantic similarity scoring against injection templates and structural analysis of delimiter patterns; uses local embedding models rather than external APIs, enabling offline detection without cloud dependencies

vs others: More specialized for LLM-specific injection vectors than generic input validation; faster than API-based detection services because it runs locally; more comprehensive than simple keyword filtering by combining multiple detection strategies

via “prompt injection detection with prompt guard”

Largest open-weight model at 405B parameters.

Unique: Prompt Guard companion tool provides dedicated prompt injection detection for 405B, enabling security-aware applications to filter adversarial inputs before inference, though requiring separate inference and orchestration

vs others: Open-source security tool allows on-premises deployment and integration into custom security pipelines; however, adds inference latency and cost compared to integrated security mechanisms in some proprietary models

via “prompt injection and capability escalation detection with multi-chain analysis”

AI agent security scanner. Detect vulnerabilities in agent configurations, MCP servers, and tool permissions. Available as CLI, GitHub Action, ECC plugin, and GitHub App integration. 🛡️

Unique: Implements multi-chain injection analysis using Claude 3.5 Opus (in deep scan mode) to simulate 'Russian Doll' attacks where an attacker chains multiple prompts to bypass restrictions; combines static pattern matching with adversarial LLM-based testing to detect both obvious and subtle injection vectors

vs others: More sophisticated than generic prompt injection detectors because it understands agent-specific attack patterns (tool escalation, system prompt override, multi-turn manipulation) and uses adversarial LLM testing to find novel injection techniques

via “prompt injection detection”

Production-ready prompt injection detection for AI agents. Scan user input, retrieved docs, and tool outputs before passing them to an LLM. Returns injection_detected, score, attack_type, and sanitized text.

Unique: Utilizes a combination of heuristic and pattern-based detection methods that adapt to various types of prompt injection attacks, making it robust against evolving threats.

vs others: More comprehensive than basic regex-based filters, as it analyzes context and intent rather than just matching patterns.

via “prompt injection detection and content filtering with configurable rules”

Local-first personal agentic OS and everything app for coding, knowledge work, web design, automations, and artifacts.

Unique: Implements multi-layer content filtering with configurable rules for prompt injection detection and output content filtering, supporting both built-in patterns and custom filter implementations, with audit logging for policy violations

vs others: More customizable than fixed content filters with rule-based approach, though less sophisticated than ML-based detection and more prone to false positives than semantic analysis

via “browser extensions and desktop applications for cross-platform access”

f.k.a. Awesome ChatGPT Prompts. Share, discover, and collect prompts from the community. Free and open source — self-host for your organization with complete privacy.

Unique: Extends prompts.chat beyond the web platform with browser extensions and desktop apps, enabling prompt access from any application or web page. The context-aware suggestion system uses selected text to recommend relevant prompts, reducing friction in the prompt selection process.

vs others: More integrated into user workflows than web-only platforms because extensions work on any website; more accessible than CLI tools because extensions provide visual UI. Differs from generic text processing tools by being specialized for prompt application.

via “prompt-injection-vulnerability-testing-and-documentation”

LEAKED SYSTEM PROMPTS FOR CHATGPT, CLAUDE, GEMINI, GROK, PERPLEXITY, CURSOR, LOVABLE, REPLIT, AND MORE! - AI SYSTEMS TRANSPARENCY FOR ALL! 👐

Unique: Catalogs obfuscated injection directives (e.g., *!<NEW_PARADIGM>!* with leetspeak payloads) as reproducible, documented attack vectors rather than one-off exploits. The repository tracks which obfuscation techniques work against which models, creating a systematic vulnerability database for prompt injection.

vs others: Provides a curated, version-specific database of working injection techniques, whereas most security research on prompt injection is scattered across academic papers and informal security disclosures without centralized tracking.

via “browser extension and userscript integration for in-context prompt access”

🚀💪Maximize your efficiency and productivity. The ultimate hub to manage, customize, and share prompts. (English/中文/Español/العربية). 让生产力加倍的 AI 快捷指令。更高效地管理提示词，在分享社区中发现适用于不同场景的灵感。

Unique: Implements dual distribution model via both formal browser extensions and Tampermonkey userscripts, enabling reach across browsers and users who prefer lightweight script-based solutions. Uses DOM manipulation to inject prompts directly into LLM interfaces, eliminating the need for API integrations with ChatGPT or Claude.

vs others: More accessible than ChatGPT plugins because it works without requiring ChatGPT Plus or plugin approval, and more flexible than native integrations because it can target multiple LLM platforms simultaneously.

via “prompt injection attack detection via structural analysis”

OpenAI Guardrails: A TypeScript framework for building safe and reliable AI systems

Unique: Uses structural and pattern-based analysis to detect injection attempts rather than relying solely on semantic similarity, enabling detection of novel injection vectors and providing detailed attack vector identification

vs others: Faster and more interpretable than semantic-only detection because it identifies specific injection patterns and markers, though less robust against sophisticated paraphrased attacks than ensemble approaches

via “prompt injection attack detection”

Security scanner MCP server that protects AI coding agents from generating vulnerable code. Features: • 275+ security rules for Python, JavaScript, TypeScript, Java, Go, Ruby, PHP, C/C++, Rust, C#, Terraform, Kubernetes • AST-based detection with tree-sitter (falls back to regex when unav

Unique: Focuses specifically on analyzing AI prompts for injection risks, a niche often neglected in broader security tools.

vs others: More specialized than general security tools that do not address AI prompt vulnerabilities.

via “prompt-injection-detection-and-mitigation”

AgenShield — AI Agent Security Platform

Unique: Implements multi-layered injection detection combining pattern matching for known attack vectors with heuristic analysis for novel attempts, rather than relying on a single detection method. Can operate in detection-only mode (logging) or enforcement mode (blocking/sanitizing).

vs others: Provides proactive injection detection before inputs reach the LLM, whereas most agent security focuses on output filtering after the LLM has already processed potentially malicious inputs

via “prompt-injection-and-jailbreak-technique-documentation”

A collection of GPT system prompts and various prompt injection/leaking knowledge.

Unique: Explicitly documents prompt injection and jailbreak techniques (e.g., GrokJailbreakPrompt.md) as part of the repository's educational mission, treating security vulnerabilities as learning opportunities rather than hiding them. The SECURITY.md file provides contribution guidelines for responsibly documenting vulnerabilities.

vs others: More transparent and educational than vendor security advisories that often withhold technical details, but less systematic than academic security research papers that provide formal vulnerability taxonomies and impact assessments.

via “screenshot capture and inline image transmission to chatgpt”

[ChassistantGPT - embeds ChatGPT as a hands-free voice assistant in the background](https://github.com/idosal/assistant-chat-gpt)

Unique: Integrates Chrome's tabs.captureVisibleTab API with ChatGPT's image upload handler via DOM injection, enabling one-click screenshot-to-ChatGPT workflow without manual file save/upload steps

vs others: Faster than manual screenshot+upload because it's a single right-click action; more seamless than external screenshot tools because it directly injects the image into ChatGPT's input field

via “prompt-injection-vulnerability-detection”

Open-source CLI security scanner for agentic workflows.

Unique: Specifically targets agentic prompt injection patterns — understands that agents are vulnerable not just through direct user input but through tool outputs that get fed back into prompts. Detects injection vectors specific to multi-turn agent reasoning where earlier tool outputs can influence later prompt execution.

vs others: More specialized than generic code injection detectors because it understands LLM-specific injection patterns and the unique threat model of agentic systems where tool outputs become prompt inputs

via “browser extension lifecycle management and dom integration”

[Talk to ChatGPT (voice interface)](https://github.com/C-Nedelcu/talk-to-chatgpt)

Unique: Uses a content script + background script architecture to intercept ChatGPT's form submission at the DOM level, allowing prompt augmentation before the API call is made. This avoids the need for API wrappers or proxies, keeping the integration lightweight and transparent to the user.

vs others: More reliable than API wrapper approaches because it operates at the UI layer where ChatGPT's actual user input is, rather than trying to intercept API calls which may be rate-limited or blocked by CORS policies.

via “dom-injection-into-ai-chat-interfaces”

One click to curate AI chatbot, including ChatGPT, Google Bard to improve AI responses.

Unique: Uses Chrome content scripts to directly manipulate the DOM of ChatGPT and Gemini interfaces rather than using APIs or iframes, enabling seamless visual integration but creating tight coupling to service UI changes.

vs others: More seamless user experience than external prompt managers because the character selector appears within the chat interface, but more fragile than API-based integration because it breaks whenever services update their DOM structure.

via “prompt injection prevention and query sanitization”

[Promptform: Run GPT in bulk](https://github.com/jasonstitt/promptform)

Unique: Implements multi-layer sanitization targeting both search API injection and LLM prompt injection, rather than treating them as separate concerns

vs others: More comprehensive than simple URL encoding, but less sophisticated than ML-based anomaly detection for prompt injection

via “prompt security and injection vulnerability detection”

Tool for prompt engineering.

via “browser-based prompt execution without backend dependencies”

A fast, no-signup playground to test and share AI prompt templates

via “browser-integrated prompt capture and injection”

Unique: Uses Chrome content script injection to embed a persistent prompt sidebar directly into ChatGPT's interface, avoiding context-switching entirely. Unlike clipboard-based tools, it maintains real-time synchronization between the web app and extension, allowing prompts saved in one context to appear instantly in another.

vs others: Faster than manual prompt management in note-taking apps because it eliminates the tab-switch overhead and integrates directly into ChatGPT's compose workflow, though it lacks the advanced features (versioning, A/B testing) of dedicated prompt engineering platforms.