promptscan
APIFreeProduction-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.
Capabilities3 decomposed
prompt injection detection
Medium confidenceThis capability scans user inputs, retrieved documents, and tool outputs for potential prompt injection attacks before they are sent to an LLM. It employs a combination of heuristic analysis and pattern recognition to identify suspicious content, returning a score indicating the likelihood of an attack, the type of attack detected, and a sanitized version of the input. This proactive approach helps maintain the integrity of AI interactions by filtering out harmful inputs.
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.
More comprehensive than basic regex-based filters, as it analyzes context and intent rather than just matching patterns.
attack type classification
Medium confidenceThis capability identifies and classifies the type of prompt injection attack detected, such as SQL injection, command injection, or data exfiltration attempts. By analyzing the structure and semantics of the input, it categorizes the threat, providing developers with actionable insights on the nature of the attack. This classification helps in tailoring responses and defenses against specific vulnerabilities.
Incorporates advanced classification algorithms that leverage both historical data and real-time analysis to improve detection accuracy over time.
More detailed than basic detection systems that only flag inputs without providing context or classification.
input sanitization
Medium confidenceThis capability sanitizes user inputs by removing or altering potentially harmful content based on the detection results. It employs a set of predefined rules and contextual understanding to ensure that the sanitized text retains its meaning while eliminating malicious components. This process is crucial for maintaining the functionality of AI models while ensuring security.
Utilizes a context-aware sanitization approach that balances security and usability, ensuring that meaningful user inputs are preserved.
More effective than simple text replacement methods, as it understands the context and intent behind user inputs.
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓developers building AI applications requiring secure input handling
- ✓security-focused developers and teams looking to enhance their AI systems
- ✓developers looking to enhance the security of their AI applications
Known Limitations
- ⚠May not catch all sophisticated injection techniques due to evolving attack vectors
- ⚠Performance may vary based on input complexity and size
- ⚠Classification accuracy may depend on the quality of training data and evolving attack strategies
- ⚠Sanitization may alter the original meaning of inputs in some cases
- ⚠Requires careful rule management to avoid over-sanitization
Requirements
Input / Output
UnfragileRank
UnfragileRank is computed from adoption signals, documentation quality, ecosystem connectivity, match graph feedback, and freshness. No artifact can pay for a higher rank.
About
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.
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