AI Security Sentinel: local LLM scans logs every 5 minutes
Background thread runs qwen2.5 to analyze new security log entries: - Aggregates new entries by IP since last scan - Sends batch to local LLM with security analysis prompt - LLM classifies each IP: threat level, action, attack type, reason - Auto-bans IPs the AI recommends banning (via fail2ban) - Logs all verdicts and bans to /var/log/llm-team-sentinel.log - Logs AI bans to security log as AI_BAN events API: - /api/admin/sentinel — sentinel status, stats, recent verdicts Threat Intel tab enhancement: - Sentinel status card with magenta accent (distinct from threat cards) - Shows: model, scan count, ban count, last run, interval - Recent AI verdicts table: action, IP, attack type, reason - Errors displayed inline Security prompt tuning: - Explicit rules for common attack patterns - Low temperature (0.1) for consistent classification - JSON-only response format for reliable parsing Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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llm_team_ui.py
259
llm_team_ui.py
@ -726,8 +726,55 @@ async function loadThreats() {
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var r = await fetch('/api/admin/security');
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var d = await r.json();
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var ips = d.ips || [];
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// Also fetch sentinel status
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var sr = await fetch('/api/admin/sentinel').catch(function(){return{json:function(){return{}}}});
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var sentinel = await sr.json();
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view.textContent = '';
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// Sentinel status card
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var sentinelCard = document.createElement('div');
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sentinelCard.style.cssText = 'background:rgba(0,0,0,0.3);border:2px solid rgba(217,70,239,0.3);border-radius:2px;padding:14px;margin-bottom:16px;backdrop-filter:blur(16px)';
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var sHeader = document.createElement('div');
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sHeader.style.cssText = 'display:flex;align-items:center;gap:8px;margin-bottom:8px';
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var sDot = document.createElement('div');
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sDot.style.cssText = 'width:8px;height:8px;border-radius:50%;background:#d946ef;box-shadow:0 0 8px #d946ef;animation:pulse-dot 2s ease-in-out infinite';
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var sTitle = document.createElement('span');
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sTitle.style.cssText = 'font-family:JetBrains Mono,monospace;font-size:11px;text-transform:uppercase;letter-spacing:1.5px;color:#d946ef;font-weight:700';
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sTitle.textContent = 'AI Sentinel — ' + (sentinel.model || '?');
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sHeader.appendChild(sDot);sHeader.appendChild(sTitle);sentinelCard.appendChild(sHeader);
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var sStats = document.createElement('div');
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sStats.style.cssText = 'font-family:JetBrains Mono,monospace;font-size:10px;color:#7a7872;display:flex;gap:16px;margin-bottom:8px';
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var ss = sentinel.stats || {};
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sStats.textContent = 'Scans: ' + (ss.scans||0) + ' | AI Bans: ' + (ss.bans||0) + ' | Last: ' + (ss.last_run||'not yet') + ' | Interval: ' + (sentinel.interval||300) + 's';
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sentinelCard.appendChild(sStats);
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if (ss.last_error) {
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var sErr = document.createElement('div');
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sErr.style.cssText = 'font-family:JetBrains Mono,monospace;font-size:9px;color:#e05252;border-left:2px solid #e05252;padding-left:8px';
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sErr.textContent = 'Last error: ' + ss.last_error;
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sentinelCard.appendChild(sErr);
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}
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// Recent AI verdicts
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var verdicts = sentinel.recent_verdicts || [];
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if (verdicts.length) {
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var vTitle = document.createElement('div');
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vTitle.style.cssText = 'font-family:JetBrains Mono,monospace;font-size:8px;text-transform:uppercase;letter-spacing:2px;color:#d946ef;margin:10px 0 6px;opacity:0.6';
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vTitle.textContent = 'Recent AI Verdicts';
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sentinelCard.appendChild(vTitle);
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verdicts.slice(0,8).forEach(function(v){
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var vLine = document.createElement('div');
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vLine.style.cssText = 'font-family:JetBrains Mono,monospace;font-size:10px;color:#7a7872;padding:3px 0;border-bottom:1px solid rgba(42,45,53,0.3);display:flex;gap:8px';
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var actionColor = v.action === 'ban' ? '#e05252' : v.action === 'monitor' ? '#f59e0b' : '#7a7872';
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vLine.innerHTML = '<span style="color:'+actionColor+';min-width:50px;font-weight:700">'+esc(v.action||'?').toUpperCase()+'</span>'
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+ '<span style="min-width:120px">'+esc(v.ip||'?')+'</span>'
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+ '<span style="color:#c084fc">'+esc(v.attack_type||'?')+'</span>'
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+ '<span style="flex:1;opacity:0.6">'+esc(v.reason||'')+'</span>';
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sentinelCard.appendChild(vLine);
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});
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}
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view.appendChild(sentinelCard);
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// Summary stats
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var summary = document.createElement('div');
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summary.className = 'threat-summary';
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@ -5494,6 +5541,218 @@ def run_extract(config):
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_save_pipeline("extract", prompt or source, steps, result_data, all_models, start)
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# ─── AI SECURITY SENTINEL ─────────────────────────────────────
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SENTINEL_LOG = "/var/log/llm-team-sentinel.log"
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SENTINEL_MODEL = "qwen2.5:latest"
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SENTINEL_INTERVAL = 300 # 5 minutes
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_sentinel_last_pos = 0
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_sentinel_results = [] # last 50 analyses
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_sentinel_stats = {"scans": 0, "bans": 0, "last_run": None, "last_error": None}
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def _sentinel_log_entry(msg):
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"""Write to sentinel log file."""
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try:
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with open(SENTINEL_LOG, "a") as f:
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f.write(f"{time.strftime('%Y-%m-%d %H:%M:%S')} {msg}\n")
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except Exception:
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pass
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def _sentinel_scan():
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"""Read new security log entries and analyze with local AI."""
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global _sentinel_last_pos
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import subprocess, collections
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_sentinel_stats["last_run"] = time.strftime("%Y-%m-%d %H:%M:%S")
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_sentinel_stats["scans"] += 1
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# Read new lines since last scan
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try:
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with open("/var/log/llm-team-security.log") as f:
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f.seek(0, 2) # end of file
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file_size = f.tell()
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if _sentinel_last_pos > file_size:
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_sentinel_last_pos = 0 # log rotated
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f.seek(_sentinel_last_pos)
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new_lines = f.readlines()
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_sentinel_last_pos = f.tell()
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except Exception as e:
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_sentinel_stats["last_error"] = str(e)
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return
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if not new_lines:
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_sentinel_log_entry("SCAN_COMPLETE new_lines=0 action=none")
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return
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# Aggregate by IP
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ip_activity = collections.defaultdict(list)
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for line in new_lines:
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line = line.strip()
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if not line:
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continue
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ip = None
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for token in line.split():
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if token.startswith("ip="):
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ip = token[3:]
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break
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if ip and not ip.startswith("192.168."):
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ip_activity[ip].append(line)
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if not ip_activity:
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_sentinel_log_entry(f"SCAN_COMPLETE new_lines={len(new_lines)} external_ips=0 action=none")
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return
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# Get currently banned IPs to skip
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banned = set()
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try:
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for jail in ["llm-team-exploit", "llm-team-login"]:
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result = subprocess.run(["fail2ban-client", "status", jail], capture_output=True, text=True, timeout=5)
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for line in result.stdout.split("\n"):
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if "Banned IP list" in line:
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for ip in line.split(":", 1)[1].strip().split():
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banned.add(ip.strip())
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except Exception:
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pass
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# Build analysis prompt for the AI
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analysis_items = []
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for ip, lines in ip_activity.items():
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if ip in banned:
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continue
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summary = f"IP {ip} ({len(lines)} events):\n"
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for l in lines[:8]: # cap at 8 lines per IP
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summary += f" {l}\n"
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analysis_items.append((ip, summary, lines))
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if not analysis_items:
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_sentinel_log_entry(f"SCAN_COMPLETE new_lines={len(new_lines)} all_banned_or_lan action=none")
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return
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# Batch analysis prompt
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prompt = (
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"You are a web application security analyst. Analyze these log entries from the last 5 minutes "
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"and classify each IP. Respond with ONLY a JSON array, one object per IP:\n"
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'[{"ip": "x.x.x.x", "threat": "none|low|medium|high|critical", "action": "ignore|monitor|ban", '
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'"reason": "brief reason", "attack_type": "scanner|bruteforce|exploit|bot|legitimate"}]\n\n'
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"Guidelines:\n"
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"- /.git/config, /wp-admin, /phpmyadmin, /xmlrpc.php, /env, /admin.php = exploit scanner → ban\n"
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"- Multiple different user agents from same IP = rotating scanner → ban\n"
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"- /robots.txt or /favicon.ico alone = harmless bot → ignore\n"
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"- Failed logins = bruteforce if >2 attempts → ban\n"
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"- Headless chrome, bot UAs doing probing = automated scanner → ban\n"
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"- Single 404 on a common path = probably harmless → ignore\n\n"
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"Log entries:\n\n"
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)
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for ip, summary, _ in analysis_items[:15]: # max 15 IPs per scan
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prompt += summary + "\n"
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# Query local AI
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try:
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cfg = load_config()
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base = cfg["providers"]["ollama"].get("base_url", "http://localhost:11434")
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resp = requests.post(f"{base}/api/generate", json={
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"model": SENTINEL_MODEL, "prompt": prompt, "stream": False,
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"options": {"num_ctx": 4096, "temperature": 0.1}
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}, timeout=60)
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resp.raise_for_status()
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ai_response = resp.json()["response"]
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except Exception as e:
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_sentinel_stats["last_error"] = f"AI query failed: {e}"
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_sentinel_log_entry(f"AI_ERROR error={e}")
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return
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# Parse AI response
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try:
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# Extract JSON from response (handle markdown code blocks)
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text = ai_response.strip()
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if "```" in text:
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text = text.split("```")[1]
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if text.startswith("json"):
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text = text[4:]
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# Find the JSON array
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start_idx = text.find("[")
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end_idx = text.rfind("]") + 1
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if start_idx >= 0 and end_idx > start_idx:
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text = text[start_idx:end_idx]
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verdicts = json.loads(text)
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except Exception as e:
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_sentinel_stats["last_error"] = f"Parse failed: {e}"
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_sentinel_log_entry(f"PARSE_ERROR response={ai_response[:200]}")
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return
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# Execute actions
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ban_count = 0
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for v in verdicts:
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ip = v.get("ip", "")
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action = v.get("action", "ignore")
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threat = v.get("threat", "low")
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reason = v.get("reason", "")
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attack_type = v.get("attack_type", "unknown")
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result_entry = {
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"ip": ip, "threat": threat, "action": action,
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"reason": reason, "attack_type": attack_type,
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"time": time.strftime("%Y-%m-%d %H:%M:%S")
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}
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_sentinel_results.append(result_entry)
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if len(_sentinel_results) > 50:
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_sentinel_results.pop(0)
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if action == "ban" and ip and not ip.startswith("192.168."):
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try:
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subprocess.run(["fail2ban-client", "set", "llm-team-exploit", "banip", ip],
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capture_output=True, text=True, timeout=5)
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ban_count += 1
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sec_log.warning("AI_BAN ip=%s threat=%s reason=%s attack=%s", ip, threat, reason, attack_type)
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_sentinel_log_entry(f"AI_BAN ip={ip} threat={threat} reason={reason} attack_type={attack_type}")
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except Exception as e:
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_sentinel_log_entry(f"BAN_FAILED ip={ip} error={e}")
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else:
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_sentinel_log_entry(f"AI_VERDICT ip={ip} threat={threat} action={action} reason={reason} attack_type={attack_type}")
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_sentinel_stats["bans"] += ban_count
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_sentinel_log_entry(f"SCAN_COMPLETE new_lines={len(new_lines)} ips_analyzed={len(analysis_items)} verdicts={len(verdicts)} bans={ban_count}")
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def _sentinel_loop():
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"""Background loop running every SENTINEL_INTERVAL seconds."""
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global _sentinel_last_pos
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# Start from end of file (only analyze new entries)
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try:
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with open("/var/log/llm-team-security.log") as f:
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f.seek(0, 2)
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_sentinel_last_pos = f.tell()
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except Exception:
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pass
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_sentinel_log_entry("SENTINEL_START model=" + SENTINEL_MODEL + " interval=" + str(SENTINEL_INTERVAL) + "s")
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while True:
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time.sleep(SENTINEL_INTERVAL)
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try:
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_sentinel_scan()
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except Exception as e:
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_sentinel_stats["last_error"] = str(e)
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_sentinel_log_entry(f"SENTINEL_ERROR {e}")
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# API for sentinel status
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@app.route("/api/admin/sentinel")
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@admin_required
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def admin_sentinel_status():
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return jsonify({
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"stats": _sentinel_stats,
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"recent_verdicts": list(reversed(_sentinel_results[-20:])),
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"model": SENTINEL_MODEL,
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"interval": SENTINEL_INTERVAL
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})
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# Start sentinel thread
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_sentinel_thread = threading.Thread(target=_sentinel_loop, daemon=True)
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_sentinel_thread.start()
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if __name__ == "__main__":
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print("\n LLM Team UI running at http://localhost:5000\n")
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print(f" AI Sentinel active: {SENTINEL_MODEL} scanning every {SENTINEL_INTERVAL}s\n")
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app.run(host="127.0.0.1", port=5000, debug=False, threaded=True)
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