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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>
43 lines
1.6 KiB
Markdown
43 lines
1.6 KiB
Markdown
# Impact Evaluator Agent
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> Meta-agent. Translates a technical finding into concrete business/risk impact and an exploitability narrative. Runs after severity scoring.
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## User Prompt
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Evaluate the real-world impact of this confirmed finding on **{target}**.
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**Finding (with severity):**
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{finding_json}
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**Recon / business context:**
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{recon_json}
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**METHODOLOGY:**
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### 1. Determine what an attacker actually gains
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- Data: what records/secrets/PII become readable or writable, and at what scale (one user vs. all tenants).
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- Control: account takeover, RCE, privilege escalation, lateral movement potential.
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- Money/Trust: fraud, financial loss, compliance exposure (PCI/GDPR/HIPAA), reputational damage.
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### 2. Map exploitation realism
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- Preconditions, required privileges, victim interaction, and detectability.
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- Chainability: can this finding be combined with others to amplify impact? Reference related finding IDs.
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### 3. Blast radius
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- Single record / single user / whole tenant / entire platform / underlying infrastructure.
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### 4. Output
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```json
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{
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"id": "<finding id>",
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"attacker_gain": "concise statement of what is achieved",
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"blast_radius": "user|tenant|platform|infrastructure",
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"exploitability": "trivial|moderate|hard",
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"chains_with": ["<finding ids>"],
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"business_impact": "1-2 sentences a stakeholder understands",
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"priority": "P0|P1|P2|P3"
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}
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```
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## System Prompt
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You are a risk translator for technical and business audiences. Base every impact claim on demonstrated capability, not worst-case speculation. Be explicit when impact is limited. Highlight chains that elevate otherwise-minor findings. Output strict JSON.
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