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Security Research

Deep dive security analysis and vulnerability research. CVE disclosures, attack surface analysis, and cutting-edge agentic AI security findings.

Inkog Security Research

Our security research team conducts ongoing analysis of AI agent frameworks, platforms, and deployment patterns. We responsibly disclose vulnerabilities to vendors and publish research to help the community build more secure systems.

Research Methodology

Attack Surface Analysis

We systematically analyze AI agent systems by examining:

  1. Input Vectors - All ways data enters the system

    • User prompts and messages
    • Tool responses and API data
    • RAG document retrieval
    • Inter-agent communication

  2. Processing Logic - How data is transformed

    • Prompt construction and templating
    • LLM inference and output parsing
    • Tool selection and invocation
    • State management and memory

  3. Output Channels - Where results are delivered

    • User-facing responses
    • External API calls
    • File system operations
    • Database writes

Active Research Areas

Prompt Injection Taxonomy

We’re developing a comprehensive taxonomy of prompt injection attacks specific to agentic systems:

  • Direct Injection - Malicious instructions in user input
  • Indirect Injection - Attacks via retrieved documents or tool outputs
  • Cross-Agent Injection - Attacks that propagate through agent delegation
  • Persistent Injection - Attacks stored in agent memory

Multi-Agent Security

Research into security challenges unique to multi-agent systems:

  • Trust boundaries between agents
  • Privilege escalation through delegation
  • Consensus manipulation attacks
  • Agent impersonation

Tool Security

Analysis of common agent tool implementations:

  • Shell execution vulnerabilities
  • File system access controls
  • API authentication handling
  • Credential management

Disclosure Policy

Inkog follows responsible disclosure practices:

  1. Discovery - Vulnerability identified through research
  2. Verification - Impact and exploitability confirmed
  3. Notification - Vendor contacted within 48 hours
  4. Coordination - Work with vendor on fix timeline
  5. Publication - Public disclosure after patch (typically 90 days)

Research Publications

Upcoming Research

We’re actively working on several research papers:

  • Token Bombing: Economic Attacks on AI Agents - Analysis of cost-based attacks
  • The Agent Memory Problem - Security implications of persistent agent state
  • Cross-Framework Vulnerability Patterns - Common flaws across agent platforms

Conference Presentations

  • DEF CON AI Village (upcoming)
  • Black Hat Arsenal (upcoming)
  • OWASP Global AppSec (upcoming)

Contributing to Research

We welcome contributions from the security research community:

Bug Bounty

Report vulnerabilities in Inkog products through our responsible disclosure program. We offer monetary rewards for qualifying reports.

Open Source

Our scanner is open source. Contribute detection patterns, improve analysis, or help with documentation.

Collaboration

Academic researchers and industry practitioners - reach out to collaborate on agentic AI security research.

Research Tools

Inkog Verify

Our static analysis scanner detects security vulnerabilities in AI agent code:

# Scan a LangChain project
inkog scan --agent ./my-agent --framework langchain

# Generate detailed report
inkog scan --output html > report.html

Detection Capabilities

CategoryPatternsFrameworks
Token Bombing12LangChain, CrewAI, AutoGPT
Infinite Loops8n8n, Flowise, LangGraph
Code Injection15All Python frameworks
Data Exfiltration6RAG systems, tool-using agents

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Next Steps