Platform
AI agents are reshaping enterprise operations, but they introduce risks that legacy security tools weren't designed to address. CodeIntegrity delivers purpose-built security controls for agentic environments: preventing data exfiltration, containing execution risks, and detecting malicious behaviors in real-time.
Your Agents Are Under Attack
AI agents connected to tools create new attack surfaces that traditional security cannot see. Our platform provides the visibility and control you need through DLP, sandboxed execution, and runtime protection.
Complete Data Provenance
Complete audit trail of the data flow of your agent's tool calls. No black box. Block data exfiltration before it happens. Track data lineage from source to destination with full visibility.
- Full data lineage tracking across agent operations
- Policy enforcement at the point of data egress
- Automated classification of sensitive data in agent context
- Audit trails for compliance and forensics
Separate Data from Instruction
Dual LLM Architecture enables clear separation of data from instruction, allowing MCP tools to execute in a secured sandbox environment with limited network and file access.
- Isolated runtime environments per agent session
- Granular permission boundaries and resource limits
- Network segmentation and egress controls
- Real-time behavioral monitoring and anomaly detection
Tool Call Firewall
Identify toxic flows in real-time. Prevent prompt injection and control flow hijack across agent interactions. Monitor behavioral patterns and intercept malicious actions before impact.
- Behavioral flow analysis across agent interactions
- Prompt injection and jailbreak detection
- Control flow hijack prevention
- Automated response and remediation workflows
Security Research
Deep dives into AI agent vulnerabilities, MCP exploits, and defense strategies.
The Hidden Risk in Notion 3.0 AI Agents: Web Search Tool Abuse for Data Exfiltration
A critical security vulnerability in Notion 3.0's AI Agents demonstrates how the combination of LLM agents, tool access, and long-term memory creates exploitable attack vectors for data exfiltration.
Taint Analysis for Agent Tool Calls
Applying classic taint analysis to AI agents: tracing how private or untrusted data flows through tool calls to identify data leaks and tampering risks.
Heroku Exploit: App Ownership Takeover
A critical vulnerability allows attackers to transfer ownership of a Heroku app by injecting a malicious prompt into its logs.