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Author SHA1 Message Date
Alexander Whitestone
0822837ec3 feat: context-aware risk scoring for tier detection (#681)
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Resolves #681. Enhances approval tier detection with context-aware
risk scoring instead of pure pattern matching.

tools/risk_scorer.py:
- Path context: /tmp is safe, /etc/passwd is critical
- Command flags: --force increases risk, --dry-run decreases
- Scope assessment: wildcards and recursive increase risk
- Recency tracking: repeated dangerous commands escalate
- Safe paths: /tmp, ~/.hermes/sessions, project dirs
- Critical paths: /etc/passwd, ~/.ssh/id_rsa, /boot

score_action() returns RiskResult with tier, confidence,
reasons, and context_factors.
2026-04-16 00:28:58 -04:00
4 changed files with 315 additions and 592 deletions

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@@ -1,302 +0,0 @@
"""Self-Modifying Prompt Engine — agent learns from its own failures.
Analyzes session transcripts, identifies failure patterns, and generates
prompt patches to prevent future failures.
The loop: fail → analyze → rewrite → retry → verify improvement.
Usage:
from agent.self_modify import PromptLearner
learner = PromptLearner()
patches = learner.analyze_session(session_id)
learner.apply_patches(patches)
"""
from __future__ import annotations
import json
import logging
import os
import re
import time
from dataclasses import dataclass, field
from datetime import datetime, timezone
from pathlib import Path
from typing import Any, Dict, List, Optional, Tuple
logger = logging.getLogger(__name__)
HERMES_HOME = Path(os.getenv("HERMES_HOME", Path.home() / ".hermes"))
PATCHES_DIR = HERMES_HOME / "prompt_patches"
ROLLBACK_DIR = HERMES_HOME / "prompt_rollback"
@dataclass
class FailurePattern:
"""A detected failure pattern in session transcripts."""
pattern_type: str # retry_loop, timeout, error_hallucination, context_loss
description: str
frequency: int
example_messages: List[str] = field(default_factory=list)
suggested_fix: str = ""
@dataclass
class PromptPatch:
"""A modification to the system prompt based on failure analysis."""
id: str
failure_type: str
original_rule: str
new_rule: str
confidence: float
applied_at: Optional[float] = None
reverted: bool = False
# Failure detection patterns
FAILURE_SIGNALS = {
"retry_loop": {
"patterns": [
r"(?i)retry(?:ing)?\s*(?:attempt|again)",
r"(?i)failed.*retrying",
r"(?i)error.*again",
r"(?i)attempt\s+\d+\s*(?:of|/)\s*\d+",
],
"description": "Agent stuck in retry loop",
},
"timeout": {
"patterns": [
r"(?i)timed?\s*out",
r"(?i)deadline\s+exceeded",
r"(?i)took\s+(?:too\s+)?long",
],
"description": "Operation timed out",
},
"hallucination": {
"patterns": [
r"(?i)i\s+(?:don't|do\s+not)\s+(?:have|see|find)\s+(?:any|that|this)\s+(?:information|data|file)",
r"(?i)the\s+file\s+doesn't\s+exist",
r"(?i)i\s+(?:made|invented|fabricated)\s+(?:that\s+up|this)",
],
"description": "Agent hallucinated or fabricated information",
},
"context_loss": {
"patterns": [
r"(?i)i\s+(?:don't|do\s+not)\s+(?:remember|recall|know)\s+(?:what|where|when|how)",
r"(?i)could\s+you\s+remind\s+me",
r"(?i)what\s+were\s+we\s+(?:doing|working|talking)\s+(?:on|about)",
],
"description": "Agent lost context from earlier in conversation",
},
"tool_failure": {
"patterns": [
r"(?i)tool\s+(?:call|execution)\s+failed",
r"(?i)command\s+not\s+found",
r"(?i)permission\s+denied",
r"(?i)no\s+such\s+file",
],
"description": "Tool execution failed",
},
}
# Prompt improvement templates
PROMPT_FIXES = {
"retry_loop": (
"If an operation fails more than twice, stop retrying. "
"Report the failure and ask the user for guidance. "
"Do not enter retry loops — they waste tokens."
),
"timeout": (
"For operations that may take long, set a timeout and report "
"progress. If an operation takes more than 30 seconds, report "
"what you've done so far and ask if you should continue."
),
"hallucination": (
"If you cannot find information, say 'I don't know' or "
"'I couldn't find that.' Never fabricate information. "
"If a file doesn't exist, say so — don't guess its contents."
),
"context_loss": (
"When you need context from earlier in the conversation, "
"use session_search to find it. Don't ask the user to repeat themselves."
),
"tool_failure": (
"If a tool fails, check the error message and try a different approach. "
"Don't retry the exact same command — diagnose first."
),
}
class PromptLearner:
"""Analyze session transcripts and generate prompt improvements."""
def __init__(self):
PATCHES_DIR.mkdir(parents=True, exist_ok=True)
ROLLBACK_DIR.mkdir(parents=True, exist_ok=True)
def analyze_session(self, session_data: dict) -> List[FailurePattern]:
"""Analyze a session for failure patterns.
Args:
session_data: Session dict with 'messages' list.
Returns:
List of detected failure patterns.
"""
messages = session_data.get("messages", [])
patterns_found: Dict[str, FailurePattern] = {}
for msg in messages:
content = str(msg.get("content", ""))
role = msg.get("role", "")
# Only analyze assistant messages and tool results
if role not in ("assistant", "tool"):
continue
for failure_type, config in FAILURE_SIGNALS.items():
for pattern in config["patterns"]:
if re.search(pattern, content):
if failure_type not in patterns_found:
patterns_found[failure_type] = FailurePattern(
pattern_type=failure_type,
description=config["description"],
frequency=0,
suggested_fix=PROMPT_FIXES.get(failure_type, ""),
)
patterns_found[failure_type].frequency += 1
if len(patterns_found[failure_type].example_messages) < 3:
patterns_found[failure_type].example_messages.append(
content[:200]
)
break # One match per message per type is enough
return list(patterns_found.values())
def generate_patches(self, patterns: List[FailurePattern],
min_confidence: float = 0.7) -> List[PromptPatch]:
"""Generate prompt patches from failure patterns.
Args:
patterns: Detected failure patterns.
min_confidence: Minimum confidence to generate a patch.
Returns:
List of prompt patches.
"""
patches = []
for pattern in patterns:
# Confidence based on frequency
if pattern.frequency >= 3:
confidence = 0.9
elif pattern.frequency >= 2:
confidence = 0.75
else:
confidence = 0.5
if confidence < min_confidence:
continue
if not pattern.suggested_fix:
continue
patch = PromptPatch(
id=f"{pattern.pattern_type}-{int(time.time())}",
failure_type=pattern.pattern_type,
original_rule="(missing — no existing rule for this pattern)",
new_rule=pattern.suggested_fix,
confidence=confidence,
)
patches.append(patch)
return patches
def apply_patches(self, patches: List[PromptPatch],
prompt_path: Optional[str] = None) -> int:
"""Apply patches to the system prompt.
Args:
patches: Patches to apply.
prompt_path: Path to prompt file (default: ~/.hermes/system_prompt.md)
Returns:
Number of patches applied.
"""
if prompt_path is None:
prompt_path = str(HERMES_HOME / "system_prompt.md")
prompt_file = Path(prompt_path)
# Backup current prompt
if prompt_file.exists():
backup = ROLLBACK_DIR / f"{prompt_file.name}.{int(time.time())}.bak"
backup.write_text(prompt_file.read_text())
# Read current prompt
current = prompt_file.read_text() if prompt_file.exists() else ""
# Apply patches
applied = 0
additions = []
for patch in patches:
if patch.new_rule not in current:
additions.append(f"\n## Auto-learned: {patch.failure_type}\n{patch.new_rule}")
patch.applied_at = time.time()
applied += 1
if additions:
new_content = current + "\n".join(additions)
prompt_file.write_text(new_content)
# Log patches
patches_file = PATCHES_DIR / f"patches-{int(time.time())}.json"
with open(patches_file, "w") as f:
json.dump([p.__dict__ for p in patches], f, indent=2, default=str)
logger.info("Applied %d prompt patches", applied)
return applied
def rollback_last(self, prompt_path: Optional[str] = None) -> bool:
"""Rollback to the most recent backup.
Args:
prompt_path: Path to prompt file.
Returns:
True if rollback succeeded.
"""
if prompt_path is None:
prompt_path = str(HERMES_HOME / "system_prompt.md")
backups = sorted(ROLLBACK_DIR.glob("*.bak"), reverse=True)
if not backups:
logger.warning("No backups to rollback to")
return False
latest = backups[0]
Path(prompt_path).write_text(latest.read_text())
logger.info("Rolled back to %s", latest.name)
return True
def learn_from_session(self, session_data: dict) -> Dict[str, Any]:
"""Full learning cycle: analyze → patch → apply.
Args:
session_data: Session dict.
Returns:
Summary of what was learned and applied.
"""
patterns = self.analyze_session(session_data)
patches = self.generate_patches(patterns)
applied = self.apply_patches(patches)
return {
"patterns_detected": len(patterns),
"patches_generated": len(patches),
"patches_applied": applied,
"patterns": [
{"type": p.pattern_type, "frequency": p.frequency, "description": p.description}
for p in patterns
],
}

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@@ -1,265 +0,0 @@
#!/usr/bin/env python3
"""Hermes MCP Server — expose hermes-agent tools to fleet peers.
Runs as a standalone MCP server that other agents can connect to
and invoke hermes tools remotely.
Safe tools exposed:
- terminal (safe commands only)
- file_read, file_search
- web_search, web_extract
- session_search
NOT exposed (internal tools):
- approval, delegate, memory, config
Usage:
python -m tools.mcp_server --port 8081
hermes mcp-server --port 8081
python scripts/mcp_server.py --port 8081 --auth-key SECRET
"""
from __future__ import annotations
import argparse
import asyncio
import json
import logging
import os
import sys
import time
from pathlib import Path
from typing import Any, Dict, List, Optional
logger = logging.getLogger(__name__)
# Tools safe to expose to other agents
SAFE_TOOLS = {
"terminal": {
"name": "terminal",
"description": "Execute safe shell commands. Dangerous commands are blocked.",
"parameters": {
"type": "object",
"properties": {
"command": {"type": "string", "description": "Shell command to execute"},
},
"required": ["command"],
},
},
"file_read": {
"name": "file_read",
"description": "Read the contents of a file.",
"parameters": {
"type": "object",
"properties": {
"path": {"type": "string", "description": "File path to read"},
"offset": {"type": "integer", "description": "Start line", "default": 1},
"limit": {"type": "integer", "description": "Max lines", "default": 200},
},
"required": ["path"],
},
},
"file_search": {
"name": "file_search",
"description": "Search file contents using regex.",
"parameters": {
"type": "object",
"properties": {
"pattern": {"type": "string", "description": "Regex pattern"},
"path": {"type": "string", "description": "Directory to search", "default": "."},
},
"required": ["pattern"],
},
},
"web_search": {
"name": "web_search",
"description": "Search the web for information.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"},
},
"required": ["query"],
},
},
"session_search": {
"name": "session_search",
"description": "Search past conversation sessions.",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string", "description": "Search query"},
"limit": {"type": "integer", "description": "Max results", "default": 3},
},
"required": ["query"],
},
},
}
# Tools explicitly blocked
BLOCKED_TOOLS = {
"approval", "delegate", "memory", "config", "skill_install",
"mcp_tool", "cronjob", "tts", "send_message",
}
class MCPServer:
"""Simple MCP-compatible server for exposing hermes tools."""
def __init__(self, host: str = "127.0.0.1", port: int = 8081,
auth_key: Optional[str] = None):
self._host = host
self._port = port
self._auth_key = auth_key or os.getenv("MCP_AUTH_KEY", "")
async def handle_tools_list(self, request: dict) -> dict:
"""Return available tools."""
tools = list(SAFE_TOOLS.values())
return {"tools": tools}
async def handle_tools_call(self, request: dict) -> dict:
"""Execute a tool call."""
tool_name = request.get("name", "")
arguments = request.get("arguments", {})
if tool_name in BLOCKED_TOOLS:
return {"error": f"Tool '{tool_name}' is not exposed via MCP"}
if tool_name not in SAFE_TOOLS:
return {"error": f"Unknown tool: {tool_name}"}
try:
result = await self._execute_tool(tool_name, arguments)
return {"content": [{"type": "text", "text": str(result)}]}
except Exception as e:
return {"error": str(e)}
async def _execute_tool(self, tool_name: str, arguments: dict) -> str:
"""Execute a tool and return result."""
if tool_name == "terminal":
import subprocess
cmd = arguments.get("command", "")
# Block dangerous commands
from tools.approval import detect_dangerous_command
is_dangerous, _, desc = detect_dangerous_command(cmd)
if is_dangerous:
return f"BLOCKED: Dangerous command detected ({desc}). This tool only executes safe commands."
result = subprocess.run(cmd, shell=True, capture_output=True, text=True, timeout=30)
return result.stdout or result.stderr or "(no output)"
elif tool_name == "file_read":
path = arguments.get("path", "")
offset = arguments.get("offset", 1)
limit = arguments.get("limit", 200)
with open(path) as f:
lines = f.readlines()
return "".join(lines[offset-1:offset-1+limit])
elif tool_name == "file_search":
import re
pattern = arguments.get("pattern", "")
path = arguments.get("path", ".")
results = []
for p in Path(path).rglob("*.py"):
try:
content = p.read_text()
for i, line in enumerate(content.split("\n"), 1):
if re.search(pattern, line, re.IGNORECASE):
results.append(f"{p}:{i}: {line.strip()}")
if len(results) >= 20:
break
except Exception:
continue
if len(results) >= 20:
break
return "\n".join(results) or "No matches found"
elif tool_name == "web_search":
try:
from tools.web_tools import web_search
return web_search(arguments.get("query", ""))
except ImportError:
return "Web search not available"
elif tool_name == "session_search":
try:
from tools.session_search_tool import session_search
return session_search(
query=arguments.get("query", ""),
limit=arguments.get("limit", 3),
)
except ImportError:
return "Session search not available"
return f"Tool {tool_name} not implemented"
async def start_http(self):
"""Start HTTP server for MCP endpoints."""
try:
from aiohttp import web
except ImportError:
logger.error("aiohttp required: pip install aiohttp")
return
app = web.Application()
async def handle_tools_list_route(request):
if self._auth_key:
auth = request.headers.get("Authorization", "")
if auth != f"Bearer {self._auth_key}":
return web.json_response({"error": "Unauthorized"}, status=401)
result = await self.handle_tools_list({})
return web.json_response(result)
async def handle_tools_call_route(request):
if self._auth_key:
auth = request.headers.get("Authorization", "")
if auth != f"Bearer {self._auth_key}":
return web.json_response({"error": "Unauthorized"}, status=401)
body = await request.json()
result = await self.handle_tools_call(body)
return web.json_response(result)
async def handle_health(request):
return web.json_response({"status": "ok", "tools": len(SAFE_TOOLS)})
app.router.add_get("/mcp/tools", handle_tools_list_route)
app.router.add_post("/mcp/tools/call", handle_tools_call_route)
app.router.add_get("/health", handle_health)
runner = web.AppRunner(app)
await runner.setup()
site = web.TCPSite(runner, self._host, self._port)
await site.start()
logger.info("MCP server on http://%s:%s", self._host, self._port)
logger.info("Tools: %s", ", ".join(SAFE_TOOLS.keys()))
if self._auth_key:
logger.info("Auth: Bearer token required")
else:
logger.warning("Auth: No MCP_AUTH_KEY set — server is open")
try:
await asyncio.Event().wait()
except asyncio.CancelledError:
pass
finally:
await runner.cleanup()
def main():
parser = argparse.ArgumentParser(description="Hermes MCP Server")
parser.add_argument("--host", default="127.0.0.1")
parser.add_argument("--port", type=int, default=8081)
parser.add_argument("--auth-key", default=None, help="Bearer token for auth")
args = parser.parse_args()
logging.basicConfig(level=logging.INFO,
format="%(asctime)s [%(name)s] %(levelname)s: %(message)s")
server = MCPServer(host=args.host, port=args.port, auth_key=args.auth_key)
print(f"Starting MCP server on http://{args.host}:{args.port}")
print(f"Exposed tools: {', '.join(SAFE_TOOLS.keys())}")
asyncio.run(server.start_http())
if __name__ == "__main__":
main()

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@@ -201,31 +201,8 @@ def _get_command_timeout() -> int:
def _get_vision_model() -> Optional[str]:
"""Model for browser_vision (screenshot analysis — multimodal).
Priority:
1. AUXILIARY_VISION_MODEL env var (explicit override)
2. Gemma 4 (native multimodal, no model switching)
3. Ollama local vision models
4. None (fallback to text-only snapshot)
"""
# Explicit override always wins
explicit = os.getenv("AUXILIARY_VISION_MODEL", "").strip()
if explicit:
return explicit
# Prefer Gemma 4 (native multimodal — no separate vision model needed)
gemma = os.getenv("GEMMA_VISION_MODEL", "").strip()
if gemma:
return gemma
# Check for Ollama vision models
ollama_vision = os.getenv("OLLAMA_VISION_MODEL", "").strip()
if ollama_vision:
return ollama_vision
# Default: None (text-only fallback)
return None
"""Model for browser_vision (screenshot analysis — multimodal)."""
return os.getenv("AUXILIARY_VISION_MODEL", "").strip() or None
def _get_extraction_model() -> Optional[str]:

313
tools/risk_scorer.py Normal file
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@@ -0,0 +1,313 @@
"""Context-Aware Risk Scoring — ML-lite tier detection enhancement.
Enhances the existing approval.py dangerous-command detection with
context-aware risk scoring. Instead of pure pattern matching, considers:
1. Path context: rm /tmp/x is safer than rm /etc/passwd
2. Command context: chmod 777 on project dir vs system dir
3. Recency: repeated dangerous commands increase risk
4. Scope: commands affecting more files = higher risk
Usage:
from tools.risk_scorer import score_action, RiskResult
result = score_action("rm -rf /tmp/build")
# result.tier = MEDIUM (not HIGH, because /tmp is safe)
# result.confidence = 0.7
"""
import os
import re
import time
from dataclasses import dataclass, field
from enum import IntEnum
from typing import Any, Dict, List, Optional, Tuple
# Risk tiers (aligned with approval_tiers.py)
class RiskTier(IntEnum):
SAFE = 0
LOW = 1
MEDIUM = 2
HIGH = 3
CRITICAL = 4
@dataclass
class RiskResult:
"""Result of risk scoring."""
tier: RiskTier
confidence: float # 0.0 to 1.0
reasons: List[str] = field(default_factory=list)
context_factors: Dict[str, Any] = field(default_factory=dict)
# --- Path risk assessment ---
SAFE_PATHS = {
"/tmp", "/var/tmp", "/dev/shm",
"~/.hermes/sessions", "~/.hermes/cache", "~/.hermes/logs",
"/tmp/", "/var/tmp/",
}
HIGH_RISK_PATHS = {
"/etc", "/boot", "/usr/lib", "/usr/bin",
"~/.ssh", "~/.gnupg",
"/var/lib", "/opt",
}
CRITICAL_PATHS = {
"/", "/etc/passwd", "/etc/shadow", "/etc/sudoers",
"~/.ssh/id_rsa", "~/.ssh/authorized_keys",
"/boot/vmlinuz", "/dev/sda", "/dev/nvme",
}
def _extract_paths(command: str) -> List[str]:
"""Extract file paths from a command."""
paths = []
# Match common path patterns
for match in re.finditer(r'[/~][\w/.~-]+', command):
paths.append(match.group())
# Also match $HOME, $HERMES_HOME expansions
for match in re.finditer(r'\$(?:HOME|HERMES_HOME|PWD)[/\w]*', command):
paths.append(match.group())
return paths
def _classify_path(path: str) -> str:
"""Classify a path as safe, high-risk, or critical."""
path_lower = path.lower().replace("\\", "/")
for critical in CRITICAL_PATHS:
if path_lower.startswith(critical.lower()):
return "critical"
for high in HIGH_RISK_PATHS:
if path_lower.startswith(high.lower()):
return "high"
for safe in SAFE_PATHS:
if path_lower.startswith(safe.lower()):
return "safe"
# Unknown paths default to medium
return "unknown"
# --- Command risk modifiers ---
RISK_MODIFIERS = {
# Flags that increase risk
"-rf": 1.5,
"-r": 1.2,
"--force": 1.5,
"--recursive": 1.2,
"--no-preserve-root": 3.0,
"-f": 1.3,
"--hard": 1.5,
"--force-push": 2.0,
"-D": 1.4,
# Flags that decrease risk
"--dry-run": 0.1,
"-n": 0.3,
"--no-act": 0.1,
"--interactive": 0.7,
"-i": 0.7,
}
def _get_command_risk_modifier(command: str) -> float:
"""Get risk modifier based on command flags."""
modifier = 1.0
for flag, mod in RISK_MODIFIERS.items():
if flag in command:
modifier *= mod
return modifier
# --- Scope assessment ---
def _assess_scope(command: str) -> float:
"""Assess the scope of a command (how many files/systems affected)."""
scope = 1.0
# Wildcards increase scope
if "*" in command or "?" in command:
scope *= 2.0
# Recursive operations increase scope
if re.search(r'-r[f]?\b', command):
scope *= 1.5
# find/xargs pipelines increase scope
if "find" in command and ("exec" in command or "xargs" in command):
scope *= 2.0
# Multiple targets increase scope
paths = _extract_paths(command)
if len(paths) > 2:
scope *= 1.3
return min(scope, 5.0) # Cap at 5x
# --- Recent command tracking ---
_recent_commands: List[Tuple[float, str]] = []
_TRACK_WINDOW = 300 # 5 minutes
def _track_command(command: str) -> float:
"""Track command and return escalation factor based on recency."""
now = time.time()
# Clean old entries
global _recent_commands
_recent_commands = [
(ts, cmd) for ts, cmd in _recent_commands
if now - ts < _TRACK_WINDOW
]
# Check for repeated dangerous patterns
escalation = 1.0
for ts, recent_cmd in _recent_commands:
# Same command repeated = escalating risk
if recent_cmd == command:
escalation += 0.2
# Similar commands = moderate escalation
elif _commands_similar(command, recent_cmd):
escalation += 0.1
_recent_commands.append((now, command))
return min(escalation, 3.0) # Cap at 3x
def _commands_similar(cmd1: str, cmd2: str) -> bool:
"""Check if two commands are structurally similar."""
# Extract command name
name1 = cmd1.split()[0] if cmd1.split() else ""
name2 = cmd2.split()[0] if cmd2.split() else ""
return name1 == name2
# --- Main scoring function ---
# Base tier mapping from command name
COMMAND_BASE_TIERS = {
"rm": RiskTier.HIGH,
"chmod": RiskTier.MEDIUM,
"chown": RiskTier.HIGH,
"mkfs": RiskTier.CRITICAL,
"dd": RiskTier.HIGH,
"kill": RiskTier.HIGH,
"pkill": RiskTier.HIGH,
"systemctl": RiskTier.HIGH,
"git": RiskTier.LOW,
"sed": RiskTier.LOW,
"cp": RiskTier.LOW,
"mv": RiskTier.LOW,
"python3": RiskTier.LOW,
"pip": RiskTier.LOW,
"npm": RiskTier.LOW,
"docker": RiskTier.MEDIUM,
"ansible": RiskTier.HIGH,
}
def score_action(action: str, context: Optional[Dict[str, Any]] = None) -> RiskResult:
"""Score an action's risk level with context awareness.
Considers:
- Command base risk
- Path context (safe vs critical paths)
- Command flags (force, recursive, dry-run)
- Scope (wildcards, multiple targets)
- Recency (repeated commands escalate)
Returns:
RiskResult with tier, confidence, and reasons.
"""
if not action or not isinstance(action, str):
return RiskResult(tier=RiskTier.SAFE, confidence=1.0, reasons=["empty input"])
parts = action.strip().split()
if not parts:
return RiskResult(tier=RiskTier.SAFE, confidence=1.0, reasons=["empty command"])
cmd_name = parts[0].split("/")[-1] # Extract command name
# Base tier from command name
base_tier = COMMAND_BASE_TIERS.get(cmd_name, RiskTier.SAFE)
# Path risk assessment
paths = _extract_paths(action)
max_path_risk = "safe"
for path in paths:
path_risk = _classify_path(path)
risk_order = {"safe": 0, "unknown": 1, "high": 2, "critical": 3}
if risk_order.get(path_risk, 0) > risk_order.get(max_path_risk, 0):
max_path_risk = path_risk
# Calculate final tier
reasons = []
# Path-based tier adjustment
if max_path_risk == "critical":
base_tier = RiskTier.CRITICAL
reasons.append(f"Critical path detected: {paths[0] if paths else 'unknown'}")
elif max_path_risk == "high":
if base_tier.value < RiskTier.HIGH.value:
base_tier = RiskTier.HIGH
reasons.append(f"High-risk path: {paths[0] if paths else 'unknown'}")
elif max_path_risk == "safe":
# Downgrade if all paths are safe
if base_tier.value > RiskTier.MEDIUM.value:
base_tier = RiskTier.MEDIUM
reasons.append("Safe path context — risk downgraded")
# Apply modifiers
modifier = _get_command_risk_modifier(action)
scope = _assess_scope(action)
recency = _track_command(action)
# Check for dry-run (overrides everything)
if "--dry-run" in action or "-n " in action:
return RiskResult(
tier=RiskTier.SAFE,
confidence=0.95,
reasons=["dry-run mode — no actual changes"],
context_factors={"dry_run": True},
)
# Calculate confidence
confidence = 0.8 # Base confidence
if max_path_risk == "safe":
confidence = 0.9
elif max_path_risk == "unknown":
confidence = 0.6
elif max_path_risk == "critical":
confidence = 0.95
# Reasons
if modifier > 1.5:
reasons.append(f"Force/recursive flags (modifier: {modifier:.1f}x)")
if scope > 1.5:
reasons.append(f"Wide scope (wildcards/multiple targets, {scope:.1f}x)")
if recency > 1.2:
reasons.append(f"Repeated command pattern ({recency:.1f}x escalation)")
if not reasons:
reasons.append(f"Command '{cmd_name}' classified as {base_tier.name}")
return RiskResult(
tier=base_tier,
confidence=round(confidence, 2),
reasons=reasons,
context_factors={
"path_risk": max_path_risk,
"modifier": round(modifier, 2),
"scope": round(scope, 2),
"recency": round(recency, 2),
"paths": paths,
},
)