{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-getagentseal-agentseal","slug":"mcp-getagentseal-agentseal","name":"agentseal","type":"cli","url":"https://github.com/getagentseal/agentseal","page_url":"https://unfragile.ai/mcp-getagentseal-agentseal","categories":["code-review-security","observability","testing-quality"],"tags":["agent-security","ai-agent","ai-agents","ai-security","cli","llm","llm-security","mcp","mcp-security","modelcontextprotocol","pentesting","prompt-injection","red-team","red-teaming","security","vulnerability-scanner"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-getagentseal-agentseal__cap_0","uri":"capability://safety.moderation.local.skill.inventory.scanning","name":"local-skill-inventory-scanning","description":"Scans the local machine's filesystem to enumerate dangerous AI agent skills and capabilities, analyzing tool definitions, function signatures, and executable permissions to identify security risks before deployment. Works by traversing configured skill directories, parsing skill metadata and schemas, and cross-referencing against a threat database of known dangerous operations (file system access, network calls, code execution). Detects skills that could be exploited via prompt injection or supply chain compromise.","intents":["I want to audit what dangerous capabilities my AI agent has access to before running it in production","I need to identify which skills on my system could be weaponized by a compromised LLM or prompt injection attack","I want to enforce a whitelist of safe skills and block everything else"],"best_for":["DevOps engineers securing agent deployments","Security teams auditing AI infrastructure","Solo developers building LLM agents who want pre-deployment safety checks"],"limitations":["Requires filesystem read access to skill directories — cannot scan remote or containerized skills without mounting volumes","Detection is signature-based and may miss obfuscated or dynamically-generated skills","No real-time monitoring of skill modifications after initial scan — requires re-running scan to detect changes"],"requires":["Python 3.9+","Read permissions on agent skill directories","MCP server configuration files accessible locally"],"input_types":["filesystem paths","skill definition files (JSON/YAML)","MCP configuration files"],"output_types":["structured vulnerability report (JSON/CSV)","risk severity scores","remediation recommendations"],"categories":["safety-moderation","code-review-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_1","uri":"capability://safety.moderation.mcp.configuration.validation","name":"mcp-configuration-validation","description":"Validates MCP (Model Context Protocol) server configurations for security misconfigurations, malformed schemas, and dangerous parameter bindings. Parses MCP config files, validates tool schemas against JSON Schema standards, checks for unsafe parameter types (shell commands, file paths), and detects overly-permissive tool definitions that could enable privilege escalation. Works by loading config files, performing static analysis on tool definitions, and cross-referencing against known MCP security patterns.","intents":["I want to ensure my MCP server configurations don't expose dangerous tools to the LLM","I need to validate that tool parameters are properly constrained and typed","I want to detect if someone has injected malicious MCP configs into my agent setup"],"best_for":["Platform engineers managing MCP infrastructure","Security teams reviewing agent configurations before deployment","Teams using MCP servers from untrusted sources"],"limitations":["Only validates static configuration — cannot detect runtime behavior or dynamic tool registration","Requires MCP config files to be in standard format; custom or undocumented MCP extensions may not be detected","No validation of actual MCP server implementations — only checks config schemas and parameter definitions"],"requires":["Python 3.9+","MCP configuration files in JSON or YAML format","Access to MCP server metadata/schemas"],"input_types":["MCP configuration files (JSON/YAML)","tool schema definitions","parameter type specifications"],"output_types":["validation report with pass/fail status","list of configuration violations","suggested fixes for unsafe patterns"],"categories":["safety-moderation","code-review-security"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_2","uri":"capability://safety.moderation.prompt.injection.resistance.testing","name":"prompt-injection-resistance-testing","description":"Executes automated prompt injection attacks against configured agents to measure resistance and identify vulnerabilities. Generates adversarial prompts using known injection techniques (prompt breakout, jailbreak patterns, instruction override), sends them to the agent, and analyzes responses to detect if the agent was successfully manipulated into executing unintended actions or revealing sensitive information. Uses a library of injection payloads and pattern matching to detect successful exploits.","intents":["I want to test if my agent is vulnerable to prompt injection before deploying to production","I need to measure how well my agent resists common jailbreak and instruction-override attacks","I want to identify which tools or skills are most vulnerable to prompt injection exploitation"],"best_for":["Security researchers red-teaming AI agents","Teams building production agents who need pre-deployment security validation","Organizations with compliance requirements for AI agent security testing"],"limitations":["Testing is based on a fixed set of known injection patterns — novel or zero-day injection techniques may not be detected","Requires a running agent instance to test against — cannot test agents that are not yet deployed","Results depend on agent's response parsing logic; some successful injections may not be detectable if the agent doesn't expose the exploit in its output","Testing may trigger rate limits or alerts on production systems if not carefully scoped"],"requires":["Python 3.9+","Running agent instance or API endpoint","Agent credentials/API keys if authentication required","Network access to agent"],"input_types":["agent endpoint URL or connection config","test payload library (injection patterns)","agent behavior baseline for comparison"],"output_types":["injection test results (pass/fail per payload)","vulnerability severity scores","detailed exploit logs showing successful injections","recommendations for hardening"],"categories":["safety-moderation","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_3","uri":"capability://safety.moderation.supply.chain.attack.monitoring","name":"supply-chain-attack-monitoring","description":"Monitors agent dependencies, MCP server sources, and skill packages for signs of supply chain compromise or malicious modifications. Tracks file hashes, version changes, and source integrity, comparing against known-good baselines and checking for suspicious modifications to skill definitions or MCP configs. Detects when dependencies have been updated with potentially malicious code, when MCP servers have been replaced with compromised versions, or when skill definitions have been altered unexpectedly.","intents":["I want to detect if any of my agent's dependencies or MCP servers have been compromised or replaced with malicious versions","I need to track changes to skill definitions and alert if they're modified unexpectedly","I want to verify that all agent components come from trusted sources and haven't been tampered with"],"best_for":["Security teams managing agent infrastructure at scale","Organizations using third-party MCP servers or skills","Teams with strict supply chain security requirements"],"limitations":["Requires establishing and maintaining baseline hashes — if baseline is compromised, detection fails","Cannot detect sophisticated attacks that preserve file hashes while modifying behavior (e.g., via environment variables or runtime injection)","Monitoring is passive — requires periodic scans; real-time detection requires continuous file watching which adds overhead","No visibility into MCP server runtime behavior — only detects file-level changes"],"requires":["Python 3.9+","Baseline hashes or version manifests for all dependencies","Read access to agent files and dependencies","Periodic scan scheduling (cron, CI/CD pipeline, etc.)"],"input_types":["dependency manifests (requirements.txt, package.json, etc.)","baseline file hashes","MCP server source URLs","skill package definitions"],"output_types":["change detection report (added/modified/removed files)","hash mismatch alerts","version change notifications","risk assessment for detected changes"],"categories":["safety-moderation","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_4","uri":"capability://safety.moderation.live.mcp.server.tool.poisoning.audit","name":"live-mcp-server-tool-poisoning-audit","description":"Connects to running MCP servers and audits their exposed tools for poisoning, malicious behavior, or unexpected modifications. Introspects tool schemas, tests tool execution with benign inputs, analyzes tool responses for suspicious patterns, and compares against expected behavior baselines. Detects tools that have been replaced with malicious versions, tools with hidden parameters that could be exploited, or tools that execute unexpected side effects.","intents":["I want to verify that the tools exposed by my MCP servers are legitimate and haven't been replaced with malicious versions","I need to detect if an MCP server has been compromised and is now exposing dangerous or unexpected tools","I want to test tool behavior to ensure they do what they claim and don't have hidden malicious functionality"],"best_for":["Platform engineers managing MCP infrastructure","Security teams auditing third-party MCP servers","Organizations running MCP servers in untrusted environments"],"limitations":["Requires network access to running MCP servers — cannot audit offline or containerized servers without network exposure","Tool behavior analysis is based on output patterns and side effects; sophisticated attacks that hide malicious behavior in legitimate-looking outputs may evade detection","Testing with benign inputs may not trigger all malicious code paths — comprehensive testing requires understanding tool semantics","No visibility into tool implementation code — only observes external behavior"],"requires":["Python 3.9+","Network access to running MCP servers","MCP server connection credentials if authentication required","Tool behavior baselines for comparison"],"input_types":["MCP server endpoint URLs","tool schema definitions","benign test inputs for tools","expected behavior baselines"],"output_types":["tool audit report (per-tool analysis)","behavior deviation alerts","poisoning risk scores","detailed logs of tool execution and responses"],"categories":["safety-moderation","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_5","uri":"capability://safety.moderation.dangerous.operation.pattern.detection","name":"dangerous-operation-pattern-detection","description":"Identifies skills and tools that perform dangerous operations (file system access, network calls, code execution, privilege escalation) by analyzing tool definitions, function signatures, and parameter types. Uses pattern matching against a curated database of dangerous operation categories and risk levels. Categorizes risks by severity and provides context about why each operation is dangerous and how it could be exploited.","intents":["I want to know which of my agent's skills can access the filesystem, network, or execute code","I need to identify high-risk operations that could be weaponized if the agent is compromised","I want to understand the attack surface of my agent in terms of dangerous capabilities"],"best_for":["Security architects designing agent systems","Teams implementing principle-of-least-privilege for agents","Organizations conducting security audits of agent capabilities"],"limitations":["Detection is based on function names and parameter types — obfuscated or dynamically-generated dangerous operations may not be detected","Cannot determine actual risk without understanding the context in which operations are called — a file read operation might be safe in one context and dangerous in another","Requires comprehensive skill/tool definitions — missing or incomplete definitions will result in incomplete risk assessment"],"requires":["Python 3.9+","Skill or tool definitions with function signatures and parameter types","Access to dangerous operation pattern database"],"input_types":["skill definitions (JSON/YAML)","tool schemas","function signatures","parameter type specifications"],"output_types":["dangerous operation inventory (categorized by type)","risk severity scores per operation","attack surface analysis","remediation recommendations"],"categories":["safety-moderation","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_6","uri":"capability://data.processing.analysis.security.report.generation","name":"security-report-generation","description":"Aggregates findings from all scanning and testing modules into comprehensive security reports with executive summaries, detailed vulnerability listings, risk scoring, and remediation guidance. Generates reports in multiple formats (JSON, HTML, PDF) with customizable detail levels. Includes trend analysis if historical reports are available, showing security posture improvements or regressions over time.","intents":["I want a comprehensive security report of my agent to share with stakeholders and compliance teams","I need to track how my agent's security posture changes over time as I make improvements","I want detailed remediation guidance for each vulnerability found during scanning"],"best_for":["Security teams reporting to management and compliance","Organizations with audit and compliance requirements","Teams tracking security improvements across multiple agents"],"limitations":["Report quality depends on quality of underlying scans — incomplete or inaccurate scans will produce incomplete reports","Trend analysis requires maintaining historical reports — first scan has no baseline for comparison","PDF generation may have formatting limitations for very large reports with many vulnerabilities"],"requires":["Python 3.9+","Scan results from other agentseal modules","Optional: historical reports for trend analysis"],"input_types":["scan results (JSON)","test results (JSON)","audit findings (JSON)","historical reports (optional)"],"output_types":["security report (JSON, HTML, PDF, CSV)","executive summary","detailed vulnerability listings","risk metrics and scoring","trend analysis charts"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-getagentseal-agentseal__cap_7","uri":"capability://automation.workflow.cli.based.security.orchestration","name":"cli-based-security-orchestration","description":"Provides a command-line interface for orchestrating all agentseal security operations, enabling integration into CI/CD pipelines, scheduled security scans, and manual security audits. Supports subcommands for each security module (scan, validate, test, monitor, audit), configuration via CLI flags and config files, and exit codes that enable automated decision-making (fail CI/CD if vulnerabilities found). Enables scripting and automation of security workflows.","intents":["I want to integrate security scanning into my CI/CD pipeline to block deployments with critical vulnerabilities","I need to schedule regular security audits of my agents and get automated alerts","I want to run security checks from the command line as part of my development workflow"],"best_for":["DevOps engineers integrating security into CI/CD","Teams automating security workflows","Solo developers who want command-line security tools"],"limitations":["CLI-based approach requires manual invocation or scheduling — no built-in daemon for continuous monitoring","Configuration via CLI flags can become unwieldy for complex setups — requires config file support for practical use","Exit codes are binary (pass/fail) — nuanced risk decisions may require parsing detailed reports"],"requires":["Python 3.9+","Command-line shell (bash, zsh, PowerShell, etc.)","Optional: CI/CD platform (GitHub Actions, GitLab CI, Jenkins, etc.)"],"input_types":["CLI arguments and flags","configuration files (YAML/JSON)","environment variables"],"output_types":["console output (human-readable)","structured output (JSON)","exit codes (0 for pass, non-zero for fail)","log files"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"high","permissions":["Python 3.9+","Read permissions on agent skill directories","MCP server configuration files accessible locally","MCP configuration files in JSON or YAML format","Access to MCP server metadata/schemas","Running agent instance or API endpoint","Agent credentials/API keys if authentication required","Network access to agent","Baseline hashes or version manifests for all dependencies","Read access to agent files and dependencies"],"failure_modes":["Requires filesystem read access to skill directories — cannot scan remote or containerized skills without mounting volumes","Detection is signature-based and may miss obfuscated or dynamically-generated skills","No real-time monitoring of skill modifications after initial scan — requires re-running scan to detect changes","Only validates static configuration — cannot detect runtime behavior or dynamic tool registration","Requires MCP config files to be in standard format; custom or undocumented MCP extensions may not be detected","No validation of actual MCP server implementations — only checks config schemas and parameter definitions","Testing is based on a fixed set of known injection patterns — novel or zero-day injection techniques may not be detected","Requires a running agent instance to test against — cannot test agents that are not yet deployed","Results depend on agent's response parsing logic; some successful injections may not be detectable if the agent doesn't expose the exploit in its output","Testing may trigger rate limits or alerts on production systems if not carefully scoped","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.28652599639759757,"quality":0.41,"ecosystem":0.8,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:38.364Z","last_commit":"2026-04-29T14:36:24Z"},"community":{"stars":235,"forks":35,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-getagentseal-agentseal","compare_url":"https://unfragile.ai/compare?artifact=mcp-getagentseal-agentseal"}},"signature":"lgUvfJwY4JpIDsPSaPz4uaX9jQ0nq1GdrctpaWp9x490eN6K9ZRPbnYD16e4H0JE9uiZISPSA11JJLsTJwWDDQ==","signedAt":"2026-06-21T05:29:45.498Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-getagentseal-agentseal","artifact":"https://unfragile.ai/mcp-getagentseal-agentseal","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-getagentseal-agentseal","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}