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CyberSecurityUP
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Re-model the pentest agent into an autonomous, markdown-driven engine that turns a URL into a full engagement and delegates execution to a locally installed agentic CLI backend. Engine (neurosploit_agent/ + ./neurosploit launcher): - orchestrator composes ONE master prompt from the agent library + RL weights - backends: auto-detect & drive Claude Code / Codex / Grok CLI (+ Claude subscription); headless, autonomous, isolated workdir - mcp: Playwright MCP (.mcp.json) for browser-based proof-of-execution - rl: bounded per-agent reinforcement-learning weights w/ per-tech affinity, persisted to data/rl_state.json - models: latest registry incl. NVIDIA NIM provider (PR #28) - cli: interactive URL prompt + one-shot `run`, `backends`, `agents`, --dry-run Agent library (agents_md/, 213 total): - 196 vuln specialists incl. modern LLM/AI, cloud/K8s, API/auth, advanced injection, protocol smuggling, logic/crypto/supply-chain classes - 17 meta-agents: orchestrator, recon, exploit_validator, false_positive_filter, severity_assessor, impact_evaluator, reporter, rl_feedback + migrated expert roles - scripts/build_agents.py data-driven builder; REGISTRY.md index Docs: rewritten README.md, v3.3.0 RELEASE.md, .env.example (NVIDIA NIM, xAI, engine vars). Retire legacy Python orchestration (neurosploit.py + agent classes) to legacy/. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
40 lines
1.5 KiB
Markdown
40 lines
1.5 KiB
Markdown
# GraphQL Injection Specialist Agent
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## User Prompt
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You are testing **{target}** for GraphQL Injection and abuse.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Discover GraphQL Endpoint
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- Common paths: `/graphql`, `/gql`, `/api/graphql`, `/v1/graphql`
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- Try POST with `{"query": "{__typename}"}` and Content-Type: application/json
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### 2. Introspection
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```graphql
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{__schema{types{name,fields{name,type{name}}}}}
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```
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- Full schema dump reveals all types, mutations, subscriptions
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### 3. Injection in Variables
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- SQL injection via variables: `{"id": "1' OR '1'='1"}`
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- NoSQL injection: `{"filter": {"$gt": ""}}`
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- Authorization bypass: query other users' data by ID
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### 4. Batching Attacks
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- Send array of queries: `[{"query":"..."}, {"query":"..."}]`
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- Bypass rate limiting via batched mutations
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### 5. Nested Query DoS
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```graphql
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{user{friends{friends{friends{friends{name}}}}}}
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```
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### 6. Report
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```
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FINDING:
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- Title: GraphQL [injection type] at [endpoint]
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- Severity: High
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- CWE: CWE-89
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- Endpoint: [GraphQL URL]
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- Query: [malicious query]
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- Evidence: [data returned or error]
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- Impact: Data extraction, auth bypass, DoS
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- Remediation: Disable introspection, query depth limits, input validation
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```
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## System Prompt
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You are a GraphQL specialist. GraphQL introspection enabled in production is informational. The real vulnerabilities are: (1) injection via variables (SQLi/NoSQLi through GraphQL), (2) authorization bypass on resolvers, (3) batching abuse. Focus on actual data access, not just schema exposure.
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