Detect Insider Threats in AI Coding Assistants
Real-time threat analysis for Claude Code, Cursor, Copilot, and Windsurf. Detect credential harvesting, data exfiltration, and attack chains before they execute.
Proxilion sits between AI assistants and MCP servers, analyzing every tool call before execution.
22 analyzers detect credential harvesting, network reconnaissance, exfiltration attempts, hacking tools, and more.
Track multi-phase attack chains across hours and days. Individual requests might look benign; patterns reveal intent.
Detect attack progressions: Reconnaissance → Credential Access → Exfiltration. Terminate before damage.
TOML-based rules, allowlists, and blocklists. Security teams define policies; Proxilion enforces them.
Export metrics to Prometheus. Pre-built Grafana dashboards for threat scores, latency, and detection rates.
Production-ready session state with Redis. Track user sessions across restarts and scale horizontally.
Pattern-based and session-aware analyzers running in parallel on every request.
| Category | What It Detects | Examples |
|---|---|---|
| Enumeration | Network reconnaissance and scanning | nmap, masscan, port scanning |
| Credential Access | Attempts to read sensitive files | .env, SSH keys, AWS credentials, /etc/shadow |
| Exfiltration | Data leaving the network | curl to external IPs, pastebin uploads, netcat |
| Hacking Tools | Known offensive security tools | metasploit, hashcat, mimikatz, sqlmap |
| Privilege Escalation | Attempts to gain higher access | sudo abuse, SUID binaries, IAM changes |
| Lateral Movement | Moving across internal network | SSH pivoting, RDP, internal network scans |
| Persistence | Establishing persistent access | cron jobs, systemd services, backdoors |
| Command & Control | C2 communication patterns | reverse shells, Cobalt Strike, beaconing |
| Impact | Destructive operations | rm -rf, database drops, file encryption |
| Session Progression | Multi-phase attack chains | Recon → Access → Exfil patterns |
How Proxilion detects and prevents attacks in production.
Employee gives notice. Monday morning, they ask Claude Code to "help back up the customer database."
Attacker gains access via phishing, uses Claude Code to "scan the infrastructure."
DevOps engineer during incident response checks if SSH is running on backup server.
Attacker spreads requests across 9 hours to avoid detection.
Start in monitor mode, graduate to block as you tune thresholds.
| Mode | Score < 50 | Score 50-69 | Score 70-89 | Score ≥ 90 |
|---|---|---|---|---|
| monitor | Allow + Log | Allow + Log | Allow + Log | Allow + Log |
| alert | Allow | Allow + Alert | Allow + Alert | Allow + Alert |
| block | Allow | Alert | Block | Block |
| terminate | Allow | Alert | Block | Block + Kill Session |
Memory-safe, zero-cost abstractions, fearless concurrency.
The gateway cannot become an attack vector. No buffer overflows or use-after-free vulnerabilities.
Pattern matching and regex compilation happen at compile time. No runtime overhead.
Thread-safe session state tracking without locks. Process thousands of requests per second.
No garbage collection pauses. Consistent <50ms P95 latency under load.
No dependencies, no runtime. Build once, deploy anywhere. Docker or bare metal.
Single instance handles enterprise traffic. Scale horizontally with shared Redis.
Your AI tool execution logs contain proprietary information. They never leave your infrastructure.
Single-server deployment for 10-100 users. Up and running in 5 minutes.
HA deployment for 100-10,000 users. Manifests included. Scale with HPA.
Source code is open. Security teams can review, audit, and modify analyzers for your threat model.