Security MCP servers for scanning, threat intelligence and audits: let an AI assistant check code, dependencies and infrastructure for risks. 3,541 servers in this category.
Enables low-level Windows x64 process memory research, including typed memory access, AOB scanning, inline hooking, and Lua automation.
Anonymizes documents locally by replacing personal data with stable tags before sending them to LLMs and restores the original data in the response.
Connects AI assistants to HackerOne to automate bug bounty hunting tasks, manage reports, and track security vulnerabilities.
Analyzes transactions for fraudulent activity using multi-model detection and Hugging Face integration to provide risk scores and insights.
Provides a secure data access layer for AI agents that enforces SQL safety, field-level masking, and authorization through the MCP protocol.
Enables AI clients to perform secure, read-only network packet analysis using tshark and capinfos across protocols like HTTP, DNS, and TLS.
Calculates cryptographic entropy, estimates GPU brute-force cracking timelines, and audits k-anonymity privacy for credentials.
Audits browser fingerprint entropy and synthesizes decoy search schedules to protect user anonymity from algorithmic profiling.
Generates tamper-evident cryptographic audit trails and Merkle proofs to ensure the integrity of agentic actions and telemetry data.
Detects agentic data exfiltration using HMAC token generation, entropy analysis, and outbound channel interception.
Audits agentic tool permissions using blast radius scoring, privilege escalation detection, and least-privilege sandboxing.
Performs agentic cybersecurity red-teaming by fuzzing for indirect prompt injections using delimiter evasion and unicode homoglyph generation.
Detects token velocity anomalies and implements adaptive rate-limiting using deterministic mathematical modeling.
Maps AI agent decision steps into a nine-digit balanced ternary cognitive state sequence to generate audit reports.
Analyzes large codebases to identify security vulnerabilities and exploit chains using AI-driven reinforcement learning.
Provides deterministic execution, cryptographic security, and game-theoretic optimization for compliance tasks via the Model Context Protocol.
