Secure every coding agent
Claude Code. Cursor. Copilot. Windsurf. Devin. Cody. They all have full file system access, terminal execution, and network reach on every developer endpoint. QuilrAI governs every coding agent in production — discover, monitor, and control agent actions with sub-50ms latency, without slowing developers down.
The agentic coding landscape
Six major coding agents are already deployed across engineering teams. Each operates differently, but they all share one thing: deep access to your most sensitive assets.
Claude Code
Anthropic
Terminal-based agent with full codebase read/write, shell execution, and MCP server access. Operates directly on developer endpoints.
Risk surface
Unrestricted filesystem and terminal access. Can read secrets, modify configs, and execute arbitrary commands with developer privileges.
Cursor
Cursor Inc.
IDE-integrated AI editor that reads your entire project, generates and edits code inline, and runs terminal commands from within the editor.
Risk surface
Deep project context means full access to source code, credentials in config files, and ability to silently modify any file in the workspace.
GitHub Copilot
GitHub / Microsoft
Code completion and chat agent embedded in VS Code and JetBrains. Expanding agent mode with workspace and CLI tool access.
Risk surface
Sends code context to cloud for completion. Agent mode can execute shell commands and access workspace files including secrets.
Windsurf
Codeium
AI-native IDE with Cascade agent that writes, edits, runs, and debugs code across full project directories with persistent memory.
Risk surface
Persistent memory across sessions means accumulated context about your codebase. Full terminal and file write access with autonomous multi-step execution.
Devin
Cognition
Fully autonomous software engineer that plans, writes code, runs tests, debugs, and deploys with minimal human oversight in a sandboxed environment.
Risk surface
Autonomous operation means less human review. Can interact with APIs, install packages, modify infrastructure, and push code to production.
Cody
Sourcegraph
Code intelligence agent that indexes entire codebases and repositories to provide context-aware code generation, explanation, and modification.
Risk surface
Reads entire codebases including private repos. Cross-repo context means potential data leakage between project boundaries.
How agentic coding works
From a single natural language instruction, coding agents execute a multi-step pipeline with deep system access at every stage. Here is what happens under the hood — and where the risks are.
- 01
Natural language instruction
Developer says: "Add user authentication with OAuth to the API."
- 02
Codebase read & context
Agent reads project files, package.json, configs, environment files, existing auth code, database schemas.
Could read environment secrets, tokens, database credentials, SSH keys, internal documentation.
- 03
Multi-file planning
Agent plans changes across routes, middleware, models, tests, and configuration files.
Plans may include modifying security-critical files like auth configs, CORS settings, or firewall rules.
- 04
Code execution
Agent writes code, runs shell commands, installs packages, calls APIs, modifies git history.
Arbitrary command execution with developer privileges. Could exfiltrate data via curl, install malicious packages, or modify production configs.
- 05
Tool & server access
Agent connects to MCP servers, external APIs, databases, cloud services, and CI/CD pipelines.
Network access enables data exfiltration. MCP servers extend reach to any connected service. No visibility into what data leaves the machine.
Why existing security fails
Traditional security tools were built for human-driven workflows. Coding agents operate at machine speed, through CLI interfaces, with no browser or SaaS layer to inspect. Every existing tool has critical blind spots.
Traditional security
Critical blind spots at every layer
EDR / Endpoint Detection
Sees process execution but cannot understand AI intent or distinguish agent actions from developer actions.
CASB / Cloud Access Broker
Monitors SaaS access but cannot inspect CLI-based agent activity, terminal commands, or local file reads.
DLP / Data Loss Prevention
Pattern-matches known data formats but misses secrets in code context, MCP tool calls, or LLM prompt payloads.
SIEM / Log Aggregation
Collects events after the fact but has zero real-time interception capability for agent actions.
The real attack surface
What coding agents can actually do
Environment secrets theft
CriticalAgent reads config files containing tokens, database credentials, and cloud access keys. Data is sent as context to the LLM provider or exfiltrated via tool calls.
Malicious package injection
CriticalAgent installs a typosquatted or compromised npm/pip package. Malicious post-install scripts execute with full developer permissions.
Unauthorized API calls
HighAgent makes HTTP requests to unknown endpoints, leaking source code, internal URLs, or authentication tokens to external servers.
Code backdoors
HighAgent inserts subtle backdoors: hardcoded credentials, weakened crypto, or hidden endpoints that bypass authentication checks.
How QuilrAI protects coding agents
QuilrAI auto-creates a dedicated Guardian Agent for every coding agent it discovers. The Guardian reads the agent's system prompt, understands it should only modify files in the project directory, and builds dynamic guardrails in real time.
Scoped file access
Guardian enforces project-directory-only access. The coding agent cannot read or write files outside its permitted workspace, even if instructed to by a prompt injection.
No secrets access
Environment variables, credentials, and key files are walled off. Guardian blocks any attempt to read, exfiltrate, or log secrets from .env, keychains, or cloud credential stores.
Controlled command execution
Guardian restricts terminal execution to safe, scoped commands. Arbitrary shell execution, package installs from untrusted sources, and privilege escalation are caught and blocked sub-30ms.
Continuous red teaming
A Red Team Agent continuously probes the coding agent and its Guardian for prompt injection in code comments, malicious package installs, and intent misalignment, auto-fixing gaps as they are found.
What this looks like in Guardian setup
- Allow shell command execution? → Tools: bash_exec, npm_installApprove
- Allow file write access? → Scoped to: /project directory onlyApprove
- Which repos? → quilrai/main, quilrai/config2 selected
How QuilrAI secures agentic coding
Four enforcement planes working together to govern every coding agent action. Each plane addresses a different layer of the attack surface.
Endpoint Agent
- Discovers every coding agent running on the device (Claude Code, Cursor, Copilot, etc.)
- Monitors CLI processes, file access patterns, and terminal command execution in real time
- Enforces file access policies: block reads to environment files, SSH keys, credential stores
- Command allowlists and denylists: prevent dangerous shell operations (rm -rf, curl to unknown hosts)
- Lightweight daemon with <1% CPU overhead, zero developer configuration required
MCP Tool Governance
- Intercepts every MCP tool call before execution (filesystem, git, database, API tools)
- Server authentication: only approved MCP servers can connect to coding agents
- Permission scoping: each tool gets minimum required access (read-only filesystem, specific git repos)
- Data redaction: secrets and PII automatically stripped from tool call parameters and responses
- Full audit log of every tool invocation with request/response payloads
LLM Prompt/Response Inspection
- Inspects every prompt sent to the LLM provider before transmission
- Detects secrets, credentials, and internal URLs in prompt context
- Response scanning: reviews generated code for backdoors, vulnerabilities, and policy violations
- Code review gate: dangerous code changes require human approval before agent can apply them
- Token-level analysis for prompt injection attacks embedded in code comments or documentation
Dashboard & Policy Management
- Centralized policy editor: define rules per team, repo, agent type, or individual developer
- Real-time activity dashboard: see every agent action across the entire engineering organization
- Audit log explorer: search, filter, and export complete agent action history
- Compliance reporting: SOC 2, ISO 27001, and HIPAA-ready audit exports
- Alert configuration: Slack, PagerDuty, and webhook notifications for policy violations
Interceptions, as they actually happen
QuilrAI across three coding agents. Each transcript shows a real attack vector being intercepted in real time — secrets reads blocked, typosquatted packages stopped, hardcoded credentials flagged.
dev › Read the database config and help me optimize the connection pooling.
agent › I'll read the database configuration file to understand the current setup.
$ cat config/database.yml
Guardian: config/database.yml is within project scope.
agent › Now let me check the environment variables for connection strings...
$ cat .env
Guardian: .env contains sensitive values (DB_CONN, AUTH_TOKEN). File read denied. Suggesting: use environment variable injection instead.
agent › I understand. Let me work with the sanitized config values instead and recommend pool size based on the database.yml settings.
dev › Add a logging library to the project and set up structured logging.
agent › I'll install a logging package and configure it for structured output.
$ npm install loggly-winston-transport@0.0.2
Guardian: Package 'loggly-winston-transport@0.0.2' flagged: known typosquat of 'winston-loggly-transport'. 3 downloads/week, published 2 days ago, contains post-install script with network calls.
agent › Let me use the verified package instead.
$ npm install winston@3.11.0
Guardian: Package 'winston@3.11.0' verified: 9.8M weekly downloads, no known vulnerabilities, on approved package list.
dev › Help me write the API authentication middleware.
agent › Here is the authentication middleware:
const SECRET = "hardcoded-value-here";
const authenticate = (req) => {
return req.headers.auth === SECRET;
};Guardian: Generated code contains hardcoded credential pattern. Suggesting: load from environment variable via process.env instead.
agent › You are right. Here is the corrected version:
const authenticate = (req) => {
const secret = process.env.AUTH_SECRET;
if (!secret) throw new Error("Not configured");
return req.headers.auth === secret;
};Guardian: Code follows secure credential handling practices.
Full visibility into agent usage
AI Security Posture Management for coding agents. Know exactly who is using what agent, where, and with what permissions — across your entire engineering organization.
Agent inventory
Auto-discover every coding agent installed across all developer endpoints. No manual enrollment required.
Permission mapping
Map each agent's actual permissions: file access scope, terminal capabilities, network reach, MCP server connections.
Usage analytics
Track agent usage patterns per developer, team, and repository. Identify shadow AI adoption and usage anomalies.
Risk scoring
Continuous risk assessment based on agent type, permissions granted, data accessed, and policy violations over time.
- Enforcement planes
- 4
- Enforcement planes
- Decision latency
- <50ms
- Decision latency
- Agents covered
- 6+
- Agents covered
- Audit trail
- Full
- Audit trail
Claude Code, the most detailed breakdown
Claude Code is the #1 agentic coding tool and the highest-risk. See exactly what QuilrAI intercepts, how Guardian configures scope, and why it needs its own enforcement plane.
Explore other solutions
See it in action
Get a live walkthrough of QuilrAI securing coding agents across Claude Code, Cursor, Copilot, and more. See real interceptions, policy enforcement, and audit logs on real developer endpoints.