OWASP Agentic AI Threats and Mitigations
Detailed coverage of how our platform addresses specific threats to AI agents.
| Threat | Description | Coverage | How We Protect You |
|---|---|---|---|
| Unauthorized Access to Resources | Agents accessing systems without proper authorization | COVERED | • x.509 cryptographic identity for each agent • Zero-trust verification for every request • Continuous authentication and access control |
| Excessive Permissions | Agents with more privileges than needed for their tasks | COVERED | • Least privilege by default through Agent Security Profiles • Granular permission controls tied to agent identity • Regular permission auditing and enforcement |
| Identity Spoofing | Impersonation of legitimate agents to gain unauthorized access | COVERED | • Tamper-proof identities with x.509 certificates • Automatic credential rotation • Anomalous identity detection |
| Uncontrolled Tool Usage | Inappropriate or unauthorized use of connected tools and APIs | COVERED | • Process execution control monitors allowed tools • Tool-specific authorization policies • Comprehensive audit logging of tool interactions |
| Data Exfiltration | Unauthorized extraction of sensitive information | COVERED | • Network and file system access monitoring • Detection of unusual data transfer patterns • Blocking of unauthorized data extraction |
| Autonomous Behavior Risks | Unpredictable or harmful agent actions without oversight | COVERED | • Behavioral boundary enforcement • Pattern deviation detection • Circuit-breaker mechanisms for unexpected behavior |
| Insecure Communication | Vulnerable data transmission between agents and systems | PARTIAL | • Monitor communication endpoints • Detect suspicious connection patterns • Cannot provide encryption for all agent communications |
| Inadequate Monitoring | Insufficient visibility into agent activities | COVERED | • Comprehensive activity logging of runtime behavior • Real-time monitoring dashboards • Suspicious activity alerting |
| Credential Exposure | Leakage of API keys, tokens, and other secrets | COVERED | • Secure secret management integration • Prevention of hardcoded credentials • Detection of credential leakage attempts |
| Insufficient Runtime Controls | Lack of real-time enforcement mechanisms | COVERED | • Realtime policy enforcement • Immediate threat response • Dynamic policy updates |
| Agent Task Hijacking | Malicious redirection of agent tasks | COVERED | • Behavioral monitoring detects deviations • Process and network controls prevent unauthorized actions • Identity-based task verification |
| Privilege Escalation | Gaining higher-level permissions than authorized | COVERED | • Syscall monitoring detects escalation attempts • Kernel-level prevention of unauthorized privilege changes • Comprehensive audit logging of privilege use |
| Resource Exhaustion | Depleting system resources causing denial of service | COVERED | • Resource consumption limits via container runtime • Per-agent quotas for CPU, memory, and network • Automatic prevention of resource abuse |
| Insecure Plugin Design | Vulnerabilities in agent plugin architecture | PARTIAL | • Monitor and control actions invoked via plugins • Detect suspicious plugin behavior • Cannot fix vulnerable plugin code itself |
| Model Theft | Unauthorized access or exfiltration of AI models | PARTIAL | • Detect suspicious file access to model files • Monitor for unusual network exfiltration patterns • Cannot prevent all sophisticated theft techniques |
OWASP Top 10 for LLMs (2025)
A comprehensive assessment of how our platform addresses the most critical threats to LLM applications.
| Threat | Description | Coverage | How We Protect You |
|---|---|---|---|
| LLM01: Prompt Injection | Attackers manipulate AI systems through crafted inputs, causing unintended behaviors | PARTIAL | • We detect and block the consequences of successful prompt injections • Monitor for unexpected process execution, file access, or network calls • Cannot prevent the initial injection itself |
| LLM02: Sensitive Information Disclosure | AI systems inadvertently reveal confidential data in their responses | PARTIAL | • Monitor and block access to sensitive files • Detect data exfiltration attempts • Cannot analyze LLM output content itself |
| LLM03: Supply Chain | Vulnerabilities introduced through model development and deployment pipelines | NOT COVERED | • Our solution focuses on runtime behavior, not model supply chain • Consider complementary solutions for this threat |
| LLM04: Data and Model Poisoning | Malicious manipulation of training data or fine-tuning processes | NOT COVERED | • Our solution does not address training data or model integrity • Consider complementary solutions for this threat |
| LLM05: Improper Output Handling | Insufficient validation of AI-generated content before use | NOT COVERED | • Our solution does not analyze or sanitize LLM outputs • Consider complementary solutions for this threat |
| LLM06: Excessive Agency | AI systems granted too much autonomy without proper constraints | COVERED | • Enforce strict boundaries on agent actions • Process, network, and file system controls tied to agent identity • Prevent unauthorized tool usage and resource access |
| LLM07: System Prompt Leakage | Exposure of internal instructions that guide AI behavior | NOT COVERED | • Our solution does not protect against prompt extraction or leakage • Consider complementary solutions for this threat |
| LLM08: Vector and Embedding Weaknesses | Security vulnerabilities in vector databases and embedding systems | NOT COVERED | • Our solution does not address vector database security • Consider complementary solutions for this threat |
| LLM09: Misinformation | Generation and spread of false or misleading information | NOT COVERED | • Our solution does not analyze content for accuracy • Consider complementary solutions for this threat |
| LLM10: Unbounded Consumption | Excessive resource usage leading to service degradation or costs | COVERED | • Implement resource usage quotas and monitoring via container runtime • Detect and prevent resource abuse patterns • Enforce computational boundaries |
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Runtime Behavior Monitoring: In depth runtime behavior monitoring and control of agent actions
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Strong Agent Identity: Cryptographic identities with x.509 certificates for every agent
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Secret Management: Secure handling of credentials and sensitive access tokens