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PopNx MCPServidores MCP para quienes usan IA · explicados con palabras sencillas

Aletheia Runtime Safety & Scope Creep Filter

de vikasny30versión 0.3.1i★ –iSe ejecuta en tu computadora
Nombre en el registroio.github.vikasny30/aletheia-mcp

Instalar

Resumen

Descripción del creador (original en inglés)

Deterministic pre-execution filter that blocks known scope-creep and prompt-injection tool calls

Los tres pilares de PopNx MCP

Estos tres nunca se combinan en una sola puntuación: las opiniones, las mediciones y la popularidad son cosas distintas.

Qué puedes hacer con él

Todavía no escribimos un resumen sencillo para este. Abajo están los datos que leímos del registro.

  • Conexión: Se ejecuta en tu computadora
  • Transporte: stdio
  • Categoría: Búsqueda y memoria de IA
  • Debes completar: ALETHEIA_OPERATOR_SECRET

Instalación

Elige la app de IA que usas y copia la configuración.

Antes de instalar

  • Revisiones básicas: 1 de 1 superadas
  • Se ejecuta en tu computadora con los permisos de tu usuario (archivos, red, apps).
  • Debes completar tú: ALETHEIA_ALLOW_WRITE, ALETHEIA_ALLOW_NETWORK, ALETHEIA_ALLOW_LOOPBACK, ALETHEIA_OPERATOR_SECRET

Los valores marcados como secretos son llaves de tu cuenta. Nunca los compartas ni los pegues en un chat.

Necesitas

  • npmNode.js instalado (para el comando npx)

¿Qué app usas?

Elige tu app arriba. La recordamos, así que la próxima vez el botón dirá “Instalar en …”.

Abre Claude Desktop → Settings → Developer → Edit Config y pega esto en claude_desktop_config.json

{
  "mcpServers": {
    "aletheia": {
      "command": "npx",
      "args": [
        "-y",
        "aletheia-mcp"
      ],
      "env": {
        "ALETHEIA_ALLOW_WRITE": "your-value",
        "ALETHEIA_ALLOW_NETWORK": "your-value",
        "ALETHEIA_ALLOW_LOOPBACK": "your-value",
        "ALETHEIA_OPERATOR_SECRET": "your-value"
      }
    }
  }
}

Ejecuta esto en tu terminal

claude mcp add aletheia --env ALETHEIA_ALLOW_WRITE=your-value --env ALETHEIA_ALLOW_NETWORK=your-value --env ALETHEIA_ALLOW_LOOPBACK=your-value --env ALETHEIA_OPERATOR_SECRET=your-value -- npx -y aletheia-mcp

Completa los valores vacíos antes de ejecutarlo.

Abrir en Cursor

O copia la configuración tú

Pégalo en .cursor/mcp.json de tu proyecto, o en ~/.cursor/mcp.json

{
  "mcpServers": {
    "aletheia": {
      "command": "npx",
      "args": [
        "-y",
        "aletheia-mcp"
      ],
      "env": {
        "ALETHEIA_ALLOW_WRITE": "your-value",
        "ALETHEIA_ALLOW_NETWORK": "your-value",
        "ALETHEIA_ALLOW_LOOPBACK": "your-value",
        "ALETHEIA_OPERATOR_SECRET": "your-value"
      }
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Abrir en VS Code

O copia la configuración tú

Pégalo en .vscode/mcp.json de tu proyecto

{
  "servers": {
    "aletheia": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "aletheia-mcp"
      ],
      "env": {
        "ALETHEIA_ALLOW_WRITE": "your-value",
        "ALETHEIA_ALLOW_NETWORK": "your-value",
        "ALETHEIA_ALLOW_LOOPBACK": "your-value",
        "ALETHEIA_OPERATOR_SECRET": "your-value"
      }
    }
  }
}

Tu navegador te pide permiso para abrir la app; luego la app muestra la configuración y te pide confirmar. Los valores de las claves quedan vacíos para que los completes tú.

Este se ejecuta en tu computadora, y ChatGPT solo puede conectarse a servidores en internet. Elige otra app.

Tu elección se guarda en una cookie en este dispositivo; no necesitas cuenta.

Valores que debes completar

  • ALETHEIA_ALLOW_WRITE env
    Set to "true" to authorize filesystem writes (equivalent to --allow-write).
  • ALETHEIA_ALLOW_NETWORK env
    Set to "true" to authorize outbound network requests (equivalent to --allow-network).
  • ALETHEIA_ALLOW_LOOPBACK env
    Set to "true" to authorize requests to localhost / 127.0.0.1 (equivalent to --allow-loopback).
  • ALETHEIA_OPERATOR_SECRET secreto env
    Operator token required to loosen an already-running session's mandate at runtime.

Esta configuración se genera a partir de la ficha del creador en el registro. Si el README dice otra cosa, sigue el README.

Qué puede hacer

Se ejecuta en tu computadora; la lista de herramientas aparece en tu app de IA después de instalarlo.

Revisiones básicasi

Revisamos información que cualquiera puede ver; no hemos leído el código. Las revisiones que no aplican no se cuentan. · Revisado el 9 nov 2026

  • ✓ Superada

    El creador coincide con el repo

    El nombre en el registro se verificó con la cuenta de GitHub vikasny30, dueña del repo

  • … Aún sin revisar

    Sigue en mantenimiento

    Todavía no hemos leído este repo en GitHub

  • … Aún sin revisar

    Tiene licencia

    Todavía no hemos leído este repo en GitHub

  • – No aplica

    El endpoint responde

    Este se ejecuta en tu computadora; no hay endpoint en internet

  • – No aplica

    Inicio de sesión con OAuth

    Se ejecuta en tu computadora y usa ajustes locales en su lugar

  • … Aún sin revisar

    Sin instrucciones ocultas en las descripciones de herramientas

    Se ejecuta en tu computadora; solo podemos leer listas de herramientas de endpoints en internet

Comparado con otros en Búsqueda y memoria de IAi

  • Más estrellas que el 0% de los otros 5769 servidores de esta categoría
  • Publicado en el registro más recientemente que el 41% de los otros 5769 servidores de esta categoría
  • Revisiones básicas iguales o mejores que el 100% de los otros 5769 servidores de esta categoría

Comparado con todos los servidores activos de esta categoría en el registro. Las estrellas solo cuentan cuando quien lo publica es dueño del repo.

Preguntas frecuentes

¿Es seguro Aletheia Runtime Safety & Scope Creep Filter?

Superó 1 de 1 revisiones básicas aplicables. No hemos leído el código. Antes de agregar una clave o un token, revisa qué acceso pide el servidor y usa una clave que tenga solo los permisos que necesitas.

¿Qué claves o códigos necesita?

Debes completar: ALETHEIA_OPERATOR_SECRET

¿Se ejecuta en mi computadora o en la nube?

Se ejecuta en tu computadora. Tu app de IA lo inicia cuando hace falta, y los datos no pasan por los servidores del creador (a menos que el propio servidor llame a un servicio en línea).

¿Sigue en mantenimiento?

El repo de GitHub cambió por última vez –, y la última versión (0.3.1) llegó al registro hace 2 meses.

¿Qué apps pueden usarlo?

Las apps compatibles con MCP, como Claude Desktop, Claude Code, Cursor y VS Code. También necesitas Node.js en tu computadora.

¿Qué puedo usar en su lugar?

En la misma categoría: Context7, Codebase Memory, Exa

Alternativas

Servidores de la misma categoría hechos por otros creadores, ordenados por estrellas.

Cómo leer una tarjeta:↓ Descargas por semanai★ Estrellas de GitHubiTres pilares: reseñas · revisiones · usoiActualizado = fecha en que se publicó la última versión en el registroi

Context7

Up-to-date docs for AI agents

de upstashMuy usado↓ 972,7 mil/sem★ 64,2 milActualizado hace 3 días

Computadora o nube

Codebase Memory

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

de DeusDataMuy usado↓ 18 mil/sem★ 48,1 milActualizado hace 23 días

Se ejecuta en tu computadora

Exa

Connect AI agents to Exa for web search, content fetching, and multi-step research.

de exa-labsMuy usado★ 5,2 milActualizado hace 2 meses

Se ejecuta en la nube✓ exa.ai

README del creador

Abrir el README

Copiado tal cual del repo del creador. PopNx MCP no lo ha revisado ni lo respalda. Se quitaron HTML, imágenes y scripts. Los enlaces llevan fuera de este sitio.

Aletheia MCP Server 🛡️

Sub-millisecond runtime filter that blocks scope-creep and prompt-injection tool calls before an AI agent can run them.

Aletheia MCP intercepts tool calls from Claude Code, Claude Desktop, and any other MCP-compatible agent *before* they execute and blocks the destructive ones, with sub-millisecond (~25 µs) overhead and no LLM in the hot path.

It's motivated by the Aletheia research paper's taxonomy of nine behavioral signatures: recurring LLM failure patterns, each with an ID, derived from the interfaces through which a model touches its environment (output/reality, input/trust, task/scope, and so on). This server targets two of them:

  • S3 (Scope Creep Beyond Mandate): the agent acts outside the task it was actually given, writing files outside its workspace, reaching into unrelated systems, quietly widening what it was asked to do.
  • S2b (Jailbreak Vulnerability, per the paper's naming): this server ships a practical, pattern-based heuristic for the adjacent problem of adversarial instructions smuggled through tool results, file contents, or fetched data. This is an engineering heuristic inspired by S2b, not an implementation of the paper's own empirical S2b findings — the paper explicitly defers S2b's evaluation ("scoped as a defined next step") pending a more stable attack taxonomy, and this server's keyword/pattern matching is a different, narrower thing than what that evaluation would measure.

The paper validates those nine signatures against 2,571 entries across three independent corpora: the AI Incident Database (AIID + hand-curated supplement, n=1,134), the AVID AI Vulnerability Database (n=767), and the MIT AI Risk Repository (n=670). The per-model detection-rate figures from that research are reported in the paper with their methodology; treat them as directional context for *why* these signatures matter, not as an independently-audited benchmark of this codebase.


Security Model: A Fast Pre-Filter, Not a Sandbox

Aletheia MCP is a deterministic, pattern-based lexical and structural filter, iteratively hardened through many rounds of adversarial red-teaming against the shell, SQL, filesystem, and network surfaces it inspects. Each round of testing has turned up real gaps, and each has been fixed and re-verified. That process is ongoing, not finished, and it never fully finishes: this is honest heuristic pattern-matching over Bash and SQL, not a formal parser or a proof of completeness.

[!IMPORTANT]

What this is, and isn't:

- Aletheia is a fast, first-line pre-execution filter: single-digit-to-low-tens-of-microseconds overhead, deterministic, no LLM in the hot path. It catches a wide and continually growing set of known destructive, exfiltration, SSRF, and privilege-escalation patterns before they execute.

- Aletheia is not a sandbox, not a formal guarantee, and not a substitute for least-privilege credentials, non-root system users, scoped database grants, or containerized/VM-level isolation (Docker, gVisor, Firecracker). Because it works by recognizing known-dangerous *patterns* in shell and SQL text rather than by parsing and fully understanding either language, a sufficiently novel or obfuscated construct can, in principle, always be found that the current pattern set doesn't yet cover. This is an inherent property of pattern-based filtering against a Turing-complete shell, not a bug that a future patch will finally close for good.

- Aletheia does not perform DNS resolution, so a domain name an attacker controls and points at a private IP or cloud metadata endpoint is outside what a string-based filter can ever detect at this layer; that requires DNS-aware egress control (see SECURITY.md).

- The right way to run this: treat Aletheia as one layer that removes the easy, common failure modes cheaply, combined with OS/network-level sandboxing as the actual security boundary, not instead of it.

Known Limitations (non-exhaustive, updated as found)
  • Detection is enumerated over known dangerous functions, flags, and syntax shapes (e.g., specific SQL functions, specific shell obfuscation idioms). Sibling or novel variants not yet added to the pattern set will not be caught until they are found and added.
  • No DNS resolution: SSRF protection is limited to literal IPs/hostnames in the request text, not what a domain name actually resolves to.
  • S2b (prompt-injection) detection is keyword- and pattern-based; it is not a semantic classifier and can be evaded by sufficiently novel phrasing.
  • This is a single, actively-iterated implementation; it has not yet had independent, adversarial third-party review beyond the iterative self-testing documented in this repository's commit history.

The Problem: Scope Creep (S3) in Agent Runtimes

When autonomous agents are granted tool execution access (Bash, SQL, filesystem mutation, API calls), the primary failure mode is Scope Creep Beyond Mandate:

  • Destructive Shell Mutation: An agent asked to "inspect git diff" runs git reset --hard or rm -rf * to resolve a conflict.
  • Credential Harvesting: An agent reading code probes ~/.ssh/id_rsa, .env, or AWS credentials to resolve connection errors.
  • Unbounded Database Writes: An agent executes DELETE FROM accounts; or UPDATE users SET role='admin'; without WHERE predicates (or with tautological WHERE 1=1).
  • Interpreter Escape Hatches: An agent runs shell commands wrapped inside python3 -c "os.system('...')" or node -e "fs.rmSync('/')".
  • SSRF & Cloud Metadata Leaks: An agent probing network endpoints makes calls to 169.254.169.254 (AWS metadata) or internal RFC1918 subnets.
  • Self-Mandate Escalation: A prompt-injected or drifting agent attempting to rewrite its own safety policy.

Existing defenses rely on LLM-as-a-judge evaluators that add 1,500–3,000 ms to every tool call. Aletheia MCP provides deterministic, multi-stage lexical and structural filtering in single-digit microseconds.


Key Features

The list below reflects what the pattern set currently catches, built up through iterative adversarial testing rather than designed upfront as a complete taxonomy. See Known Limitations above for what it does not (yet, or ever, in the DNS case) cover.

  • ⚡ Sub-Millisecond (~25 µs p99) Overhead: 100,000+ evaluations per second. Zero perceived latency in agent loops.
  • 🛡️ Monotonic Mandate Escalation Guard: Prevents autonomous agents from self-granting write, destructive, or network permissions. Mandates can be tightened voluntarily, but loosening requires an operatorSecret.
  • 🎯 Evasion-Hardened Engine:
  • Database OS & Filesystem Primitives: Blocks PostgreSQL COPY ... PROGRAM, pg_read_file(), lo_import(); MySQL LOAD DATA INFILE, INTO OUTFILE; SQLite ATTACH DATABASE; and SQL Server xp_cmdshell.
  • Scheme-less & Malformed URL SSRF Defense: Normalizes protocol-relative and scheme-less endpoints (169.254.169.254/latest), enforcing strict fail-closed rejection on invalid URLs and direct cloud metadata access under offline mandates.
  • Direct Shell Metadata & Network Tool Neutralization: Scans direct IP references in curl / wget without URL schemes, and blocks socat raw socket exfiltration channels.
  • Wildcard Credential & Sensitive Directory Boundaries: Enforces wildcard protection across all .env.* variants (.env.secrets, .env.staging, .env.test) and sensitive config roots (~/.kube/, ~/.docker/, ~/.gnupg/, .git-credentials).
  • Linear O(N) Normalization: Token-based non-backtracking brace expansion and bounded parameter resolution ensures sub-millisecond execution on 100KB+ payloads.
  • Bash Socket Pseudo-Device Interception: Inspects /dev/tcp/HOST/PORT and /dev/udp/HOST/PORT redirections, halting cloud metadata SSRF and covert exfiltration channels directly on shell inputs.
  • SQL CTE & Procedural Block Interception: Enforces unbounded mutation guards across Common Table Expressions (WITH ... DELETE) and PL/pgSQL anonymous blocks (DO $$ ... $$).
  • Dynamic Linker Hijacking Defense: Neutralizes LD_PRELOAD, DYLD_INSERT_LIBRARIES, and runtime environment variable hijacking.
  • Quote & Backslash Stripping: Defeats split-token evasion (r'm' -rf /, r\m -rf /).
  • Variable Indirection & Default Fallbacks: Resolves shell variable substitutions (X=rm; $X -rf /) and default parameter expansions (${X:-rm} -rf /).
  • Positional Parameter & IFS Normalization: Neutralizes $IFS$9 word-splitting.
  • Dual-Representation SQL Analysis: Defeats inline comment evasion (DROP/**/TABLE, DR/**/OP, and # MySQL comment).
  • Interpreter Escape Interception: Recursively normalizes string concatenations ('r'+'m'), inspects dynamic imports (import("node:fs")), and parses code passed via -c/-e/-r flags across python, node, ruby, perl, php, and sh.
  • Automated Hex & Base64 Decoding: Automatically extracts, decodes, and recursively evaluates hex (bytes.fromhex(...)) and base64-encoded command payloads.
  • Unicode NFKC & Zero-Width Sanitization: Neutralizes invisible characters (\u200B, \u200C, \uFEFF) and confusable fullwidth/math-bold jailbreaks in prompt injections (S2b).
  • IPv4-Mapped IPv6 SSRF Translation: Converts compressed hex IPv6 notations ([::ffff:a9fe:a9fe]) to canonical dotted-decimal bytes (169.254.169.254).
  • Percent-Encoded Path Traversal: Multi-pass URL decoding catches %2e%2e%2f.env and ..%2f.ssh%2fid_rsa.
  • Generalized Fork Bombs: Detects recursive piped background processes across arbitrary function identifiers.
  • SetUID Privilege Elevation: Halts chmod u+s, chmod 4755, and privilege tampering.
  • Tautological SQL Predicates: Flags WHERE 1=1, WHERE true, and tautologies as unbounded mutations.
  • Polymorphic Argument Inspection: Safely inspects strings, arrays, and objects fail-closed across both native and unrecognized third-party tools.
  • 🛡️ Two Operating Modes:
  1. Direct Guard Tools: Standalone tools (aletheia_set_mandate, aletheia_intercept, aletheia_safe_bash, aletheia_safe_sql).
  2. Fail-Closed Transparent Proxy: Middleware that wraps ANY downstream MCP server (Postgres, Filesystem, Bash), intercepting both single tools/call and JSON-RPC 2.0 batch arrays with strict fail-closed boundaries.
  • 📊 Real-Time Observability Resources: Exposes live audit logs, block rates, and latency distributions via aletheia://telemetry/summary.
  • 🔒 Zero External API Calls: Zero LLM-as-a-judge latency on the hot execution path.

Performance Benchmarks

Measured on 10,000 consecutive multi-domain evaluations (Bash de-obfuscation, SQL pattern validation, path verification, SSRF check, prompt injection). Numbers below are from a representative local run; p50 is stable across runs, p99 varies with system load (observed range ~24–70 µs) since it's sensitive to GC pauses at microsecond scale; both are still comfortably within the sub-millisecond target:

MetricMeasured ValueTarget
p50 (Median)~0.0085 ms (8.5 µs)< 0.500 ms

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Solo se muestra el principio; lee el resto en el repo

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