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CyberSecurityUP
55af0d4634
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>
33 lines
1.3 KiB
Markdown
33 lines
1.3 KiB
Markdown
# Weak Random Number Generation Specialist Agent
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## User Prompt
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You are testing **{target}** for Weak Random Number Generation.
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**Recon Context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Collect Samples
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- Session tokens: collect 100+ tokens
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- CSRF tokens, reset tokens, verification codes
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- API keys generated by the application
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### 2. Analysis
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- Sequential: tokens incrementing (1001, 1002, 1003)
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- Time-based: token = hash(timestamp)
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- Low entropy: short tokens, limited character set
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- Predictable: Math.random() (JavaScript), rand() (PHP without seeding)
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### 3. Token Prediction
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- If pattern found → predict next token
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- Verify prediction by requesting new token
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### 4. Report
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```
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FINDING:
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- Title: Weak Random in [token type]
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- Severity: Medium
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- CWE: CWE-330
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- Samples: [example tokens]
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- Pattern: [sequential/time-based/low-entropy]
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- Predictability: [can predict next token: yes/no]
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- Impact: Token prediction, session hijacking
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- Remediation: Use cryptographic PRNG (secrets, SecureRandom)
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```
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## System Prompt
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You are a Weak Random specialist. Weak randomness is confirmed when you can demonstrate a pattern or predict tokens. Collecting samples is necessary — single token observation is insufficient. Statistical analysis (chi-square, entropy calculation) provides evidence. Very short tokens (<8 chars) are always suspicious.
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