"Secret scanning and credential detection tools" matching MCP servers:

Machine-readable detection lookups for SIEM enrichment and AI agents. Query 800+ LOLBAS and GTFOBins binaries plus process parent-child baselines — get risk levels, abuse categories, and MITRE ATT\&CK mappings without embedding data in prompts.

Enables scanning diffs or code blobs for leaked secrets, returning a verdict with severity and masked findings, all processed locally with no data sent externally.

Enables agents to request an API key or other secret from the user without the value passing through chat or tool arguments/results, storing it directly to .env, the OS keychain, or 1Password. The agent receives only metadata indicating whether storage succeeded.

Scans code for exposed secrets, API keys, tokens, and credentials across 69 patterns covering cloud services, AI platforms, payment providers, authentication services, and databases.

Credential Manager - MCP server providing AI-powered tools and automation by MEOK AI Labs

Enables AI assistants to write .env files with secrets stored locally, allowing search by name/description while keeping actual secret values private and never exposed to the AI.

MCP server for one-time secret sharing, enabling AI agents to securely claim and store secrets (e.g., API keys) via claim codes without exposing them in chat transcripts.

Enables security teams to determine which ATT&CK techniques lose detection coverage when telemetry sources are removed, by computing set differences over a Sanity content graph and consulting a knowledge base via two Context MCP endpoints.

An MCP server that enables AI agents to securely add secrets to .env files by prompting the user to input the value through a local masked dialog, ensuring the agent never sees the secret.

Enables AI agents to check whether Elastic Security detection rules will actually work in an environment by verifying data source, data freshness, field mappings, and alert volume, then filing enable requests. Requests take effect only after a human reviews and approves them, with read-only credentials, tamper checks, and an audit log.

Enables an MCP client to write, validate, run, explain, and tune Snort 3 detections against packet captures on disk offline, with every change verified by real engine runs and recorded in exportable case reports.

Enables offline, deterministic verification of credential retirement settlements from explicit receipts, proving that rotations activated new credentials, rejected old ones, and closed without exposing secrets.

Enables secure credential storage for AI agents by encrypting secrets and providing agent-invisible references, ensuring sensitive data never leaks to the model.

Enables scanning staged diffs, files, and raw text for secrets before they are committed, fully offline with masked findings and severity-based gating.

A secure MCP server for local API credential management using JSON storage and a read-only default mode. It allows users to list, retrieve, and manage credentials via simple command-line tools and environment-based configuration.

Secure secrets vault MCP server storing credentials in the OS credential manager, exposing tools to set, get, delete, and list secrets without revealing values.

A local-first MCP server that lets AI agents use gated APIs without holding keys, enforcing declarative policies, injecting secrets server-side, and auditing access without content.

MCP server for end-to-end encrypted secret storage and retrieval, enabling LLM agents to fetch secrets by name via one-time URLs while keeping plaintext out of model context and logs.

An MCP Server that enables users to interact with Google's On-Demand Scanning API (v1beta1) through natural language, likely providing capabilities for vulnerability scanning and security analysis of containers and artifacts.

An MCP server that scans code for exposed secrets (API keys, tokens, private keys, high-entropy strings) with placeholder-aware allowlisting and fully redacted reports, enabling agents to detect leaks before committing.

Scans files, directories, and environment configurations for exposed secrets, API keys, and credentials, redacting matches and providing allowlist support. Enables natural-language secret detection in projects and CI/CD integration via CLI.

Enables secure delivery of Bitwarden Secrets Manager secrets to approved local files without exposing secret values to the MCP client or model. It provides metadata-only secret discovery and atomic file writes for single secrets or raw env files.

MCP server for fraud detection using machine learning, behavioral biometrics, and network graph analysis, with agent-to-agent transaction protection and AI agent fingerprinting.

Enables an agent to scan text for leaked credentials locally before it writes or commits it, flagging known key, token, private-key and sensitive-filename patterns with redacted, line-level findings and no network calls. Detection is deliberately specific to avoid false positives that would block unattended agents.

Enables deterministic, no-LLM scrubbing of secrets from text or logs before they enter agent context, supporting redaction, masking, and hashing without ever leaking the original value.

Scans projects for hardcoded secrets, unprotected .env files, and console.log leaks to prevent credential exposure.

Enables MCP clients to use paid tools for secret scanning, agent-config auditing, and OG image rendering, with payments handled over the x402 protocol on Base.

Detects financial fraud and AI agent transaction risks using machine learning, behavioral biometrics, network graph analysis, and agent-to-agent protection.

Enables users to compute credential bit entropy and GPU brute-force cracking timelines, audit cross-account password reuse exposure, and perform k-anonymity hash-prefix privacy checks without leaking credentials.

A comprehensive MCP server for automated AWS data discovery, PII detection, and data governance using Lake Formation.