Injection remains a top concern in OWASP’s 2025 Top 10, now ranked fifth but still among the most commonly exploited categories. Injection attacks occur when untrusted input is sent to an interpreter as part of a command or query, allowing attackers to manipulate application logic, gain unauthorized access, or compromise sensitive government data systems. For federal agencies, these risks must be proactively managed for both traditional applications and rapidly emerging AI-powered platforms.
What is Injection?
Injection attacks target points where external input is trusted to drive system behavior. While classic interpreters include SQL databases, OS shells, LDAP directories, and web browsers, modern threats extend to AI engines and model control protocols (MCPs). Attackers now inject malicious instructions directly into LLM prompts, agent workflows, AI tool metadata, or external content streams, causing models to leak sensitive information, produce manipulated results, or trigger unauthorized actions.
Common Examples
- SQL Injection: Attackers inject SQL statements to access, modify, or destroy database records
- OS Command Injection: Malicious input enables command execution on the underlying system
- Cross-site Scripting (XSS): Unsanitized user input manipulated to execute scripts in a victim’s browser
- AI Prompt Injection: Crafted prompts or context data hijack LLM or agent output, bypassing safety checks and leaking sensitive data
- MCP Tool Poisoning: Manipulation of tool metadata or registry data deceives model workflows to activate unauthorized or malicious tools
- Indirect Injection: Instructions planted in websites, emails, ticket systems, or cached sources to covertly steer AI behavior or responses
Federal Impact and Compliance Focus
Injection attacks are responsible for some of the most notorious breaches and manipulations in federal history. Exploited vulnerabilities can cause unauthorized disclosure, loss of mission assurance, operational disruption, and privacy violations. With agencies integrating AI systems and MCPs into mission workflows, the attack surface expands prompting a need for rigorous input validation, monitoring, and interpreter hardening. FISMA, CISA, and NIST emphasize that input validation and safe context construction are essential for both legacy and AI-driven environments.
Key Technical Weaknesses
CWE Reference | Example Flaws |
CWE-89 | SQL Injection |
CWE-79 | Cross-site Scripting (XSS) |
CWE-77 | OS Command Injection |
CWE-90 | LDAP Injection |
CWE-91 | XML Injection |
CWE-94 | Code Injection |
CWE-116 | Improper Encoding or Escaping of Output |
Visual: Injection Attack Patterns
Type | Entry Point | Impact |
SQLi | Form fields, querystrings | Data theft, unauthorized changes |
OS Command | API input, web forms, URL parameters | System takeover, service denial |
XSS | User comments, message input | Credential theft, persistent compromise |
AI Prompt | Web content, user chats, external sources | Data leakage, safety bypass, output hijack |
MCP Tool | Registry metadata, agent workflow | Unauthorized action, manipulator escalation |
Indirect | Emails, web pages, ticket histories | Stealth model manipulation, federated impact |
Practical Steps for Federal Environments
- Implement Strong Input Validation: Sanitize all entry points for expected input formats, including AI context objects and all prompt sources.
- Apply Safe Interpreter and Model Practices: Use parameterized queries, avoid dynamic code or model prompt construction from untrusted data, and enforce strict boundary controls.
- Sanitize and Encode Output: Encode all output reflecting user input, including LLM completions and generative AI responses.
- Automated Security Testing: Deploy static, dynamic, and adversarial testing tools from RavenTek’s partners to scan for injection flaws in code, configurations, and AI agent logic.
- Model and Agent Context Hardening: Restrict sources of context feeding into AI models and MCPs, audit external integrations, and monitor for manipulation attempts.
- Continuous Vulnerability Assessment: Schedule regular penetration testing focused on both classic input/output pathways and emerging AI system interpreters.
- Secure Error Handling: Eliminate overly detailed error disclosures that can aid attacker reconnaissance, for both web applications and AI models.
How RavenTek and Partners Help
RavenTek partners with leaders in automated scanning, adversarial AI testing, and secure code review. We identify, remediate, and prevent injection vulnerabilities in software and AI environments across federal missions.
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