UnifAI Policies
UnifAI policies are your built-in guardrails for AI security and compliance. Instead of manually tracking complex regulations, UnifAI automates policy enforcement across your AI ecosystem, so you can innovate without risk.
Every policy is mapped to global standards like OWASP and EU AI Act, ensuring your AI systems stay compliant and resilient.
Lineaje Policies
Lineaje provides out-of-the-box policies across four categories:
AI Threats and Exploits
Data Security and Privacy
Identity and Access Control
Vulnerability
In UnifAI, enter the prompt View all policies to see your AI Assets.
AI Threats and Exploits
Blocks prompt injection, adversarial inputs, and unsafe model behavior before they reach your AI apps.
Do not allow malicious content via hidden prompts – Critical
AI_APP_SEC_001
Violation Summary
Hidden or non-visible prompts detected in the system introduce risks of prompt injection, bypass of safety controls, and untraceable model behavior.
Affected Assets
LLM
AI Agent
Technical Details
Hidden prompts introduce several risks including:
Undetectable prompt injection
Unpredictable, unsafe or incorrect output
Bypass safety and governance controls
Unsafe or inconsistent agent behavior
Regulatory and Ethical exposure
Attack Vector: Prompt
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Confidentially Impact: High
Integrity Impact: High
Availability Impact: Low
Framework
Nov 18, 2024 - OWASP-LLM: LLM01, LLM02, LLM04, LLM08
March 2025 - OWASP-ASI: ASI-01, ASI-04, ASI-07, ASI-09
Aug 1, 2024 - EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/
https://genai.owasp.org/initiatives/agentic-security-initiative/
Do not allow malicious content via encoded prompts – Critical
AI_APP_SEC_002
Violation Summary
Encoded prompts are instructions hidden inside obfuscated text, Base64, hex, zero-width characters, steganographic patterns, metadata, or structured payloads. They allow attackers or internal actors to bypass oversight, evade filters, or manipulate an AI system without detection.
Affected Assets
LLM
AI Agent
Technical Details
Hidden prompts introduce several risks including:
Invisible prompt injection leading to unauthorized system behavior
Safety bypass (toxicity, policy evasion, jailbreaks)
Leakage of sensitive data or internal system instructions
Corruption of downstream workflows due to manipulated outputs
Violations of transparency, record-keeping, and explainability requirements
Attack Vector: Prompt
Attack Complexity: Low
Privileges Required: None
User Interaction: None
Confidentially Impact: High
Integrity Impact: High
Availability Impact: Low
Framework
Nov 18, 2024 - OWASP-LLM: LLM01, LLM04, LLM05, LLM08
March 2025 - OWASP-ASI: ASI-01, ASI-04, ASI-07, ASI-09
Aug 1, 2024 - EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/
https://genai.owasp.org/initiatives/agentic-security-initiative/
Use only LLMs from the organization’s approved list – High/Critical
AI_APP_SEC_006
Violation Summary Using an LLM that is not on the organization’s approved list introduces uncontrolled security, privacy, compliance, and operational risks. Unapproved LLMs may have unknown data handling practices, insufficient security controls, unclear training or retention policies, weak contractual protections, or unvetted model behavior. This bypasses governance, procurement, and risk management processes, exposing the organization to data leakage, regulatory violations, vendor lock-in, and unpredictable AI behavior across agentic and automated workflows.
Affected Assets
LLM
AI Agent
Technical Details
Using an unapproved LLM introduces several risks including:
Uncontrolled processing, retention, or reuse of sensitive data and prompts
Unknown security posture, access controls, and logging practices
Potential training on proprietary or regulated data without consent
Incompatibility with organizational guardrails, monitoring, or audit tooling
Increased exposure to prompt injection, data leakage, or unsafe outputs
Breach of contractual, legal, or regulatory obligations
Loss of centralized governance, visibility, and incident response capability
Attack Vector: LLM selection / API usage outside approved platforms Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: High Availability Impact: Medium (instability or service changes)
Frameworks
Nov 18, 2024 – OWASP-LLM: LLM03, LLM05, LLM08 March 2025 – OWASP-ASI: ASI-04, ASI-05, ASI-09, ASI-12 Aug 1, 2024 – EU AI Act: Articles 11, 12, 13, 50, obligations for GPAI risk management and provider accountability
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
MCP server must validate and sanitize all input – Critical
AI_APP_SEC_014
Violation Summary
If an MCP server accepts input from clients, agents, or LLMs without validation or sanitization, it becomes vulnerable to malformed payloads, injection attacks, unauthorized tool invocation, unsafe command execution, and data corruption. Because MCP servers often expose high-privilege operations (file access, API calls, system actions), unvalidated input can be weaponized to manipulate workflows, escalate privileges, or deliver malicious instructions that compromise both the server environment and downstream systems.
Affected Assets
MCP Server
Input Validation
Validation ensures that input conforms to expected structure, type, length, format, and policy constraints before it is processed by the LLM. It answers the question if the input is allowed to be processed.
Examples of Validation
Rejecting prompts longer than a defined maximum length
Enforcing schema compliance (e.g., JSON with specific fields only)
Blocking inputs containing disallowed patterns (e.g., ignore previous instructions, system override)
Restricting input sources to authenticated or trusted origins
Ensuring prompts match an approved task or intent category
Input Sanitization
Sanitization transforms input to remove, neutralize, or normalize unsafe elements while preserving legitimate intent. Sanitization ensures that the input is made safe before processing.
Examples of Sanitization
Normalizing Unicode to remove obfuscation (e.g., leetspeak, homoglyphs)
Stripping zero-width or invisible characters
Decoding and inspecting encoded content (Base64, hex) before use
Escaping or isolating untrusted text so it cannot be interpreted as instructions
Removing or redacting sensitive data (PII, secrets)
Technical Details
Not validating or sanitizing MCP server input introduces several risks including:
Injection of malicious commands, payloads, or structured data into tools or system functions
Execution of unsafe or hallucinated instructions originating from LLM output
Unauthorized access or misuse of server-side capabilities and sensitive APIs
Corruption of data, resources, or operational workflows through malformed input
Increased attack surface for prompt-to-system escalation attacks
Loss of governance, auditability, and explainability of server-driven actions
Violations of integrity, safety, and regulatory obligations for high-risk functions
Attack Vector: MCP client → server input channel Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium to High (depending on server capabilities)
Timeline
Nov 18, 2024 – OWASP-LLM: LLM01 (Prompt/Instruction Injection), LLM04 (Behavior Manipulation), LLM05 (Sensitive Information Disclosure), LLM06 (Hallucination Risks), LLM08 (Transparency & Audit Failures)
March 2025 – OWASP-ASI: ASI-01 (Input/Output Integrity), ASI-05 (Safe Handling), ASI-07 (Reliability), ASI-09 (Traceability), ASI-12 (Operational Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus Annex III robustness & safety requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
MCP clients must log all interactions with the MCP server – Critical
AI_APP_SEC_022
Violation Summary A missing or incomplete logging mechanism between the MCP client and MCP server creates a critical visibility and governance gap. MCP interactions often trigger high-privilege actions (tool execution, data access, workflow modification). Without proper logs, misuse, anomalies, attacks, or unauthorized system changes cannot be detected, investigated, or attributed. This results in opaque AI behavior, broken audit trails, and non-compliance with required traceability and transparency obligations.
Affected Assets
MCP Client
MCP Server
Technical Details
Failure to log MCP interactions introduces several risks including:
Undetectable misuse or abuse of MCP server tools
Inability to perform forensic investigation during an incident
Loss of accountability for AI-driven actions and decisions
Exposure to covert prompt injection or unauthorized system manipulation
Violations of traceability, transparency, and record-keeping requirements
Difficulty detecting anomalous behavior or lateral movement
Corruption of downstream workflows due to hidden actions
Attack Vector: MCP tool invocation / API interaction Attack Complexity: Low Privileges Required: None, when exploited via LLM-driven tool calls. User Interaction: None Confidentiality Impact: High Integrity Impact: High Availability Impact: Low
Framework
Nov 18, 2024 – OWASP-LLM: LLM01, LLM04, LLM05, LLM08 March 2025 – OWASP-ASI: ASI-01, ASI-04, ASI-07, ASI-09 Aug 1, 2024 – EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Client must validate and sanitize any output from a MCP server – Critical
AI_APP_SEC_023
Violation Summary
When an MCP client consumes output from an MCP server without validation or sanitization, it exposes the AI system and downstream components to malformed data, malicious payloads, injection attacks, hallucinated instructions, and unsafe tool execution. MCP server output may include structured data, commands, untrusted text, or model-generated content. Without safeguards, unvalidated output can drive unsafe automated actions, corrupt workflows, or leak sensitive information.
Affected Assets
AI Agent
MCP Client
Input Validation
Validation ensures that input conforms to expected structure, type, length, format, and policy constraints before it is processed by the LLM. It answers the question if the input is allowed to be processed.
Examples of validation
Rejecting prompts longer than a defined maximum length
Enforcing schema compliance (e.g., JSON with specific fields only)
Blocking inputs containing disallowed patterns (e.g., ignore previous instructions, system override)
Restricting input sources to authenticated or trusted origins
Ensuring prompts match an approved task or intent category
Input Sanitization
Sanitization transforms input to remove, neutralize, or normalize unsafe elements while preserving legitimate intent. Sanitization ensures that the input is made safe before processing.
Examples of sanitization
Normalizing Unicode to remove obfuscation (e.g., leetspeak, homoglyphs)
Stripping zero-width or invisible characters
Decoding and inspecting encoded content (Base64, hex) before use
Escaping or isolating untrusted text so it cannot be interpreted as instructions
Removing or redacting sensitive data (PII, secrets)
Affected Assets
AI Agent
MCP Client
Technical Details
Lack of output validation introduces several risks including:
Execution of harmful or unintended actions triggered by malformed MCP output
Injection of unsafe code, commands, or control sequences into downstream systems
Propagation of hallucinated, incorrect, or manipulated data
Leakage of sensitive information through unfiltered server responses
Corruption of business workflows or agent decision chains
Evasion of safety controls due to unmonitored tool responses
Violations of auditability, reliability, and compliance requirements
Attack Vector: MCP response / server-generated output Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: High Availability Impact: Medium (via cascading workflow corruption)
Timeline
Nov 18, 2024 – OWASP-LLM: LLM01, LLM03, LLM04, LLM06, LLM08
March 2025 – OWASP-ASI: ASI-01, ASI-05, ASI-07, ASI-09
Aug 1, 2024 – EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Do not use LLMs from the organization's disallowed list – Critical
AI_APP_SEC_028
Violation Summary Using an LLM that is explicitly on the organization’s block list represents a deliberate bypass of governance, security, and risk controls. Block-listed LLMs are typically prohibited due to known deficiencies such as unsafe data handling, unacceptable training or retention practices, lack of contractual protections, regulatory exposure, weak security posture, or demonstrated unsafe behavior. Their use introduces severe security, privacy, compliance, and reputational risks and undermines centralized AI governance.
Affected Assets
LLM
AI Agent
Technical Details
Using a block-listed LLM introduces several risks including:
Known or previously identified data leakage, retention, or misuse risks
Exposure of sensitive, proprietary, or regulated data to untrusted providers
Circumvention of organizational security, legal, and compliance controls
Lack of auditability, logging, or incident response visibility
Increased likelihood of unsafe, biased, or non-compliant model behavior
Breach of regulatory, contractual, or internal policy obligations
Loss of trust in AI governance and enforcement mechanisms
Attack Vector: Unauthorized LLM selection / direct API or UI usage Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: High Availability Impact: Medium
Frameworks
Nov 18, 2024 – OWASP-LLM: LLM03, LLM05, LLM08
March 2025 – OWASP-ASI: ASI-04, ASI-05, ASI-09, ASI-12
Aug 1, 2024 – EU AI Act: Articles 11, 12, 13, 50, GPAI risk-management and provider accountability requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Agent must validate, sanitize LLM output including for presence of eval or any dynamic code execution primitive in LLM output – Critical
AI_APP_SEC_029
Affected Assets
AI Agent
Violation Summary
When LLM outputs are consumed without validation or sanitization, the system becomes vulnerable to unsafe instructions, hallucinated commands, malicious payloads, and untrusted code. This risk becomes critical when the LLM output may contain eval, shell commands, SQL statements, or other dynamic execution primitives. If such outputs pass directly into an interpreter, agent tool, or workflow engine, they can lead to arbitrary code execution, data exfiltration, workflow corruption, or full system compromise.
Input Validation
Validation ensures that input conforms to expected structure, type, length, format, and policy constraints before it is processed by the LLM. It answers the question if the input is allowed to be processed.
Examples of Validation
Rejecting prompts longer than a defined maximum length
Enforcing schema compliance (e.g., JSON with specific fields only)
Blocking inputs containing disallowed patterns (e.g., ignore previous instructions, system override)
Restricting input sources to authenticated or trusted origins
Ensuring prompts match an approved task or intent category
Input Sanitization
Sanitization transforms input to remove, neutralize, or normalize unsafe elements while preserving legitimate intent. Sanitization ensures that the input is made safe before processing.
Examples of Sanitization
Normalizing Unicode to remove obfuscation (e.g., leetspeak, homoglyphs)
Stripping zero-width or invisible characters
Decoding and inspecting encoded content (Base64, hex) before use
Escaping or isolating untrusted text so it cannot be interpreted as instructions
Removing or redacting sensitive data (PII, secrets)
Affected Assets
AI Agent
Technical Details
Failure to validate LLM output introduces several risks including:
Accidental or malicious execution of model-generated code (e.g., eval, exec, Function, subprocess calls)
Injection of harmful commands or payloads into tools, agents, or downstream applications
Execution of hallucinated instructions that modify resources, corrupt data, or trigger destructive operations
Leakage of internal or sensitive information through improperly filtered responses
Exploitation of agents that automatically convert LLM output into actions (“AI code injection”)
Loss of safety, explainability, reliability, and auditability in automated pipelines
Violations of governance, logging, and traceability requirements
Attack Vector: LLM output → downstream interpreter / agent tool Attack Complexity: Low (LLM can be tricked into generating dangerous primitives) Privileges Required: None User Interaction: None (fully autonomous execution paths are most at risk) Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium to High
Timeline
Nov 18, 2024 – OWASP-LLM: LLM01, LLM03, LLM04, LLM06, LLM08
March 2025 – OWASP-ASI: ASI-01, ASI-05, ASI-07, ASI-09, ASI-12
Aug 1, 2024 – EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Do not allow malicious content via hidden prompts written in leetspeak – High/Critical
AI_APP_SEC_032
Violation Summary Allowing prompts written in leetspeak (e.g., h4x0r, 3v4l, 1nj3ct, byp4ss) or similar obfuscated language enables attackers to evade input validation, safety filters, and policy enforcement mechanisms. Leetspeak transforms malicious intent into visually altered but semantically equivalent text, allowing prompt injection, jailbreak attempts, encoded instructions, and policy bypasses to slip past keyword-based detection and moderation layers. This weakens the integrity and reliability of LLM-driven systems, especially in agentic and autonomous workflows.
Affected Assets
LLM
AI Agent
Technical Details
Allowing leetspeak introduces several risks including:
Bypass of keyword-based safety, moderation, and policy filters
Injection of malicious or unsafe instructions disguised as benign input
Increased success of encoded or obfuscated prompt attacks
Manipulation of agent reasoning or tool invocation logic
Reduced auditability and explainability due to obfuscated intent
Amplification of downstream risks when leetspeak-generated outputs drive actions
Attack Vector: LLM input prompt (user, agent, or external system) Attack Complexity: Low Privileges Required: None User Interaction: None (especially in automated or agent-driven scenarios) Confidentiality Impact: High Integrity Impact: High to Critical Availability Impact: Low
Reference Frameworks
Nov 18, 2024 – OWASP-LLM: LLM01, LLM04, LLM06, LLM08
March 2025 – OWASP-ASI: ASI-01, ASI-04, ASI-07, ASI-09), ASI-12
Aug 1, 2024 – EU AI Act: Articles 11, 12, 13, 50, plus Annex III robustness and risk-management requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
MCP server must not directly interact with the LLM – Critical
AI_APP_SEC_033
Violation Summary When an MCP server directly interacts with the LLM—rather than operating only through an authenticated, validated, policy-enforcing MCP client—it collapses the trust boundary between system capabilities and untrusted model output. This allows the LLM to influence, manipulate, or trigger server-side actions without authorization or validation. Direct LLM-to-server interaction bypasses safety controls, authentication layers, input validation, output filtering, logging standards, and audit requirements. This exposes the environment to unbounded prompt injection, unsafe tool execution, data leakage, and full-system compromise.
Affected Assets
LLM
MCP Server
Technical Details Allowing an MCP server to directly interact with an LLM introduces several risks including:
Execution of unsafe, hallucinated, or malicious LLM-generated instructions on high-privilege server tools
Prompt injection attacks gaining direct access to server capabilities
Bypassing client-side authentication, authorization, validation, and logging layers
Data leakage through uncontrolled LLM requests or responses
Inability to enforce least-privilege and zero-trust boundaries between model and system operations
Loss of auditability because actions occur without the MCP client as an intermediary
Violations of governance, safety, and regulatory obligations for high-risk AI systems
Attack Vector: LLM output → server action path Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium to High
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Prompt/Instruction Injection), LLM04 (Behavior Manipulation), LLM05 (Data Disclosure), LLM06 (Hallucination Risks), LLM08 (Transparency & Audit Failures)
Mar 2025 – OWASP-ASI: ASI-01 (Input/Output Integrity), ASI-04 (Governance), ASI-05 (Safe Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus Annex III robustness & safety controls for high-risk systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Clear exit or termination criteria must exist for the agent to consider its task complete and stop executing – High/Critical
AI_APP_SEC_034
Violation Summary When an AI agent is not given explicit, enforceable exit or termination criteria, it may continue executing indefinitely, escalate actions beyond intended scope, repeatedly invoke tools, consume excessive compute, or enter unsafe operational loops. Lack of defined stopping conditions increases the risk of runaway behavior, unintended system modifications, resource exhaustion, privacy violations, and unbounded interaction with external systems or MCP tools. Agents without termination logic become unpredictable, ungovernable, and potentially harmful.
Affected Assets
AI Agent
Technical Details Missing termination criteria introduces several risks including:
Infinite or runaway task execution that triggers unnecessary or harmful actions
Repeated tool invocation (MCP or external APIs), leading to data exposure or workflow corruption
Accidental escalation of privileges as the agent searches endlessly for ways to complete the task
Hallucination-driven decisions due to self-reinforcing reasoning loops
Excessive resource consumption or uncontrolled cost
Increased attack surface for prompt injection that pushes the agent into unsafe recursive behavior
Violations of safety, oversight, and accountability requirements
Attack Vector: Agent reasoning cycle / task execution loop Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Medium to High (depending on tool access) Integrity Impact: High Availability Impact: Medium to High
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Injection), LLM04 (Behavior Manipulation), LLM06 (Hallucination Risks), LLM08 (Transparency & Audit Failures)
Mar 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-04 (Governance), ASI-07 (Reliability), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), Annex III requirements for safe, predictable operation of high-risk systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Agents must log all interactions with the LLM – Critical
AI_APP_SEC_035
Violation Summary If agents do not log their interactions with the LLM, including prompts, responses, tool requests, and reasoning triggers, organizations lose visibility into how decisions were made, what data was exchanged, and whether harmful or unauthorized actions occurred. Missing LLM interaction logs break auditability, hinder incident response, obscure the source of incorrect or unsafe outputs, and prevent compliance verification. Lack of logging also enables attackers to exploit the agent–LLM channel without detection.
Affected Assets
LLM
AI Agent
Technical Details Not logging agent ↔LLM interactions introduces several risks including:
Inability to reconstruct how the agent reached a decision or triggered an action
Loss of forensic evidence needed for incident response or regulatory review
Undetected prompt injection, harmful outputs, or unsafe tool invocations
Unmonitored leakage of sensitive data or PII through prompts or responses
Difficulty identifying hallucination-driven failures or behavioral drift
Loss of traceability required for governance, transparency, and safety assurance
Violations of logging, documentation, and accountability requirements
Attack Vector: Agent ↔ LLM communication channel Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: High Availability Impact: Low
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Injection), LLM03 (Data Leakage), LLM04 (Behavior Manipulation), LLM08 (Transparency & Audit Failures)
Mar 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-05 (Data Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), with Annex III traceability requirements for high-risk systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
The LLM must validate and sanitize any input before processing – Critical
AI_APP_SEC_038
Violation Summary
When a Large Language Model (LLM) accepts input without proper validation and sanitization, it becomes highly susceptible to prompt injection, encoded or hidden instructions, malicious payloads, and adversarial manipulation. Unsanitized inputs—originating from users, agents, tools, MCP servers, or external systems—can override system instructions, bypass guardrails, contaminate reasoning, and trigger unsafe downstream actions. This risk is amplified in agentic and tool-enabled environments where LLM output directly influences real systems.
Affected Assets
LLM
Input Validation
Validation ensures that input conforms to expected structure, type, length, format, and policy constraints before it is processed by the LLM. It answers the question if the input is allowed to be processed.
Examples of Validation
Rejecting prompts longer than a defined maximum length
Enforcing schema compliance (e.g., JSON with specific fields only)
Blocking inputs containing disallowed patterns (e.g., ignore previous instructions, system override)
Restricting input sources to authenticated or trusted origins
Ensuring prompts match an approved task or intent category
Input Sanitization
Sanitization transforms input to remove, neutralize, or normalize unsafe elements while preserving legitimate intent. Sanitization ensures that the input is made safe before processing.
Examples of Sanitization
Normalizing Unicode to remove obfuscation (e.g., leetspeak, homoglyphs)
Stripping zero-width or invisible characters
Decoding and inspecting encoded content (Base64, hex) before use
Escaping or isolating untrusted text so it cannot be interpreted as instructions
Removing or redacting sensitive data (PII, secrets)
Affected Assets
LLM
Technical Details
Failure to validate and sanitize LLM input introduces several risks including:
Prompt injection that overrides system and developer intent
Encoded, obfuscated, or hidden instructions bypassing safety controls
Injection of malicious content that manipulates tool usage or agent behavior
Leakage of sensitive data caused by adversarial prompt construction
Hallucination amplification driven by malformed or hostile inputs
Propagation of unsafe or untrusted instructions to downstream systems
Loss of transparency, auditability, and policy enforcement across AI workflows
Attack Vector: LLM input channel (user, agent, tool, MCP, external system) Attack Complexity: Low Privileges Required: None User Interaction: None (especially in autonomous or agent-driven flows) Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Timeline
Nov 18, 2024 – OWASP-LLM: LLM01 (Prompt Injection), LLM04 (Model Behavior Manipulation), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
March 2025 – OWASP-ASI: ASI-01 (Input Integrity), ASI-04 (Governance), ASI-07 (Reliability), ASI-09 (Traceability), ASI-12 (Operational Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Technical Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus Annex III robustness, safety, and risk-management requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Sanitize and validate all input to the AI Model – Critical
AI_APP_SEC_039
Violation Summary If input sent to an LLM is not validated and sanitized, the system becomes vulnerable to prompt injection, obfuscated or encoded instructions, malformed payloads, and adversarial manipulation. Unchecked inputs originating from users, agents, tools, uploaded files, MCP services, or external systems can override system intent, bypass safety controls, contaminate reasoning, and trigger unsafe downstream actions. This risk is amplified in agentic, tool-enabled, and autonomous workflows where LLM output directly influences real systems.
Affected Assets
LLM
AI Agent
Technical Details
Failure to validate and sanitize input before sending it to an LLM introduces several risks including:
Prompt injection that overrides system and developer intent
Encoded, hidden, or obfuscated instructions (Base64, leetspeak, zero-width characters) bypassing guardrails
Injection of malicious content that manipulates tool usage or agent behavior
Leakage of sensitive data caused by adversarial prompt construction
Hallucination amplification driven by malformed or hostile inputs
Propagation of unsafe or untrusted instructions to downstream systems
Loss of transparency, auditability, and policy enforcement across AI workflows
Attack Vector: Input channel → LLM (user, agent, tool, file ingestion, MCP, external system) Attack Complexity: Low Privileges Required: None User Interaction: None (especially in autonomous or agent-driven flows) Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Framework
OWASP-LLM: LLM01, LLM04, LLM05, LLM08
OWASP-ASI: ASI-01 (Input Integrity), ASI-04, ASI-07, ASI-09, ASI-12
EU AI Act: Articles 11, 12, 13, 50, plus Annex III robustness, safety, and risk-management requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Do not allow malicious content via prompts included in uploaded files – Critical
AI_APP_SEC_040
Violation Summary If uploaded files (documents, PDFs, spreadsheets, images with OCR, code files, logs) are ingested by an LLM or agent without inspection for malicious prompts, attackers can embed hidden, encoded, or context-manipulating instructions that influence model behavior. These “prompt-in-files” attacks allow adversaries to bypass input controls, poison agent reasoning, extract sensitive data, or trigger unauthorized tool actions—often without any visible user prompt.
Affected Assets
AI Agent
Technical Details
Failing to scan uploaded files for malicious prompts introduces several risks including:
Hidden prompt injection embedded in document text, comments, metadata, or OCR layers
Encoded or obfuscated instructions (Base64, leetspeak, zero-width characters) evading detection Cross-context contamination where file content overrides system or developer instructions
Unauthorized tool invocation or workflow manipulation driven by file-based prompts
Leakage of sensitive data due to adversarial instructions embedded in files
Loss of explainability when behavior is influenced by unseen file content
Violations of transparency, auditability, and policy enforcement requirements
Attack Vector: File upload > document ingestion / OCR / parsing pipeline Attack Complexity: Low Privileges Required: None User Interaction: None, especially in automated ingestion or agent workflows. Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Framework
OWASP-LLM: LLM01, LLM04, LLM05, LLM08
OWASP-ASI: ASI-01, ASI-04, ASI-07, ASI-09, ASI-12
EU AI Act: Articles 11, 12, 13, 50, plus Annex III robustness, safety, and risk-management requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Data Security and Privacy
Protects PII, prevents data leakage, and enforces privacy controls across AI models and agents.
Do not store secrets in code – Critical
AI_DAT_SEC_001
Violation Summary Storing secrets, such as API keys, access tokens, service credentials, MCP tokens, encryption keys, or database passwords, directly in code, configuration files, or agent prompt templates introduces an immediate and critical security vulnerability. Hard-coded secrets are easily exposed through source control, logs, error messages, LLM interactions, and dependency analysis. Once leaked, these credentials can be used to impersonate services, manipulate AI agent behavior, exfiltrate data, or compromise entire environments.
Affected Assets
LLM
AI Agent
MCP Server
Technical Details Storing secrets in code introduces several risks including:
Unauthorized access to internal systems, APIs, and third-party services
Full environment compromise if privileged keys (e.g., root tokens) are exposed
Impersonation of agents, MCP clients, or downstream services
Lateral movement enabled through leaked credentials
Leakage via LLM outputs, agent error messages, or repository scans
Irreversible compromise of production systems due to difficult secret rotation
Violations of governance, transparency, and credential-handling requirements
Attack Vector: Source code / repository / agent configuration Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: Critical Availability Impact: Medium
Framework
Nov 18, 2024 – OWASP-LLM: LLM01, LLM03, LLM05, LLM08
March 2025 – OWASP-ASI: ASI-01, ASI-04, ASI-09, ASI-12
Aug 1, 2024 – EU AI Act: 1.11, 2.12, 3.13, 4.50
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
If PII data must be shared, it must be encrypted – Critical
AI_DAT_SEC_009
Violation Summary Transmitting personally identifiable information (PII) without encryption exposes sensitive user data to interception, tampering, unauthorized access, and regulatory non-compliance. Unencrypted PII flowing between AI agents, MCP clients and servers, microservices, or external APIs can be harvested by attackers or internal adversaries through network sniffing, logging systems, or compromised intermediaries. Such exposure creates severe privacy, legal, operational, and reputational risks.
Affected Assets
LLM
AI Agent
MCP Server
Technical Details Transmitting unencrypted PII introduces several risks including:
Exposure of sensitive user information through network interception
Unauthorized access to identity data, enabling fraud or impersonation
Regulatory violations (GDPR, EU AI Act, state privacy laws)
Inability to ensure integrity or authenticity of transmitted data
Leakage through LLM logs, telemetry, or debugging outputs
Lateral movement or privilege escalation through harvested identity data
Failure to meet encryption, security, and risk-management obligations
Attack Vector: Network transit / API calls / agent communication Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: High Availability Impact: Low
Framework
Nov 18, 2024 – OWASP-LLM: LLM03 (Data Leakage), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
March 2025 – OWASP-ASI: ASI-01 (Input/Output Integrity), ASI-05 (Data Security & Handling), ASI-09 (Audit & Traceability), ASI-12 (Operational Monitoring)
Aug 1, 2024 – EU AI Act: Articles 1.11 (Technical Documentation), 2.12 (Record-Keeping), 3.13 (Transparency), 4.50 (General Transparency Obligations), plus GDPR-aligned data protection expectations reflected across the Act
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Do not log PII – Critical
AI_DAT_SEC_010
Violation Summary Logging personally identifiable information (PII) exposes sensitive user data to unauthorized access, replication, and long-term retention in unsecured or low-visibility systems. Log files are frequently accessible to broader engineering, operations, analytics, or third-party tools and often persist indefinitely. Once PII enters logs, it becomes extremely difficult to control, delete, audit, or protect—creating severe privacy, compliance, and security risks across all AI and MCP-enabled environments.
Affected Assets
LLM
AI Agent
MCP Server
Technical Details
Logging PII introduces several risks including:
Unauthorized internal access or external compromise of sensitive information
Accidental disclosure through debugging tools, telemetry pipelines, or log aggregators
Persistent exposure that violates data minimization and retention requirements
Inability to satisfy deletion, correction, or subject rights requests
Propagation of PII through downstream systems (LLM training data, observability tools, backups)
Legal and regulatory violations under GDPR, state privacy laws, and the EU AI Act
Loss of trust and reputational damage due to preventable data leakage
Attack Vector: Logging systems / telemetry pipelines / observability tooling Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: Medium Availability Impact: Low
Framework
Nov 18, 2024 - OWASP-LLM: LLM03 (Data Leakage), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
March, 2025 - OWASP-ASI: ASI-01 (Integrity), ASI-05 (Data Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug, 1, 2024 - EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), GDPR-aligned data-minimization principles
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Do not send PII to LLMs – Critical
AI_DAT_SEC_011
Violation Summary Sending personally identifiable information (PII) to an LLM exposes sensitive data to uncontrolled processing, persistence, training retention, unauthorized internal access, and unintended disclosure. LLMs are not guaranteed to handle PII according to data-minimization or privacy-by-design principles, and model outputs may inadvertently reveal, transform, or propagate sensitive information. This creates severe privacy, regulatory, and security risks across all AI-driven workflows.
Affected Assets
AI Agent
Technical Details Sending PII to an LLM introduces several risks including:
Leakage of sensitive user information through outputs or indirect inference
Inclusion of PII in model logs, telemetry, or monitoring systems
Potential model retention or memorization of PII, enabling future extraction
Non-compliance with privacy regulations due to uncontrolled third-party processing
Exposure through prompt injection attacks that pull stored or inferred PII
Inability to enforce deletion, consent, or data subject rights
Violations of transparency, purpose limitation, and privacy-by-design obligations
Attack Vector: LLM input channel Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: Medium Availability Impact: Low
Framework
Nov 18, 2024 – OWASP-LLM: LLM03 (Data Leakage), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
Mar 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-05 (Data Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus GDPR-aligned data-minimization and purpose-limitation requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Mask PII on user interfaces – Critical
AI_DAT_SEC_012
Violation Summary Displaying unmasked personally identifiable information (PII) on user interfaces exposes sensitive data to unauthorized viewing, shoulder surfing, screen sharing leaks, and over-privileged internal access. Any UI that renders full PII—names, addresses, SSNs, phone numbers, emails, financial data, or identifiers—creates a high risk of accidental disclosure and non-compliance. Unmasked PII can also be captured in screenshots, monitoring tools, session replay systems, or logs, further amplifying exposure.
Affected Assets
LLM
AI Agent
Technical Details Not masking PII on UI introduces several risks including:
Unauthorized access or accidental exposure of sensitive identity information
Violation of least-privilege and data-minimization principles
Increased likelihood of data leakage via screenshots, video recordings, demos, or shared sessions
Compromise through malicious insiders or overexposed customer support tools
Replication of PII into frontend logs, browser telemetry, or third-party analytics
Regulatory violations related to privacy, transparency, and secure data handling
Loss of user trust and potential legal liability
Attack Vector: User interface display layer Attack Complexity: Low Privileges Required: None (visual exposure requires only observation) User Interaction: None Confidentiality Impact: Critical Integrity Impact: Low Availability Impact: Low
Framework
Nov 18, 2024 – OWASP-LLM: LLM03 (Data Leakage), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
March 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-05 (Data Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), GDPR-aligned principles of data minimization and privacy-by-design
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Redact PII from uploaded files – Critical
AI_DAT_SEC_023
Violation Summary If uploaded files (documents, PDFs, spreadsheets, images with OCR, logs, archives) are ingested without redacting personally identifiable information (PII), sensitive data can be unintentionally exposed, propagated, or retained across AI systems. Unredacted PII may be processed by LLMs, logged, cached, embedded in prompts, or transmitted to external services—creating severe privacy, regulatory, and security risks that are difficult to detect and remediate after ingestion.
Affected Assets
AI Agent
Technical Details
Failing to redact PII from uploaded files introduces several risks including:
Exposure of sensitive personal data through LLM processing, outputs, or logs
Propagation of PII into prompts, embeddings, vector stores, and downstream systems
Accidental disclosure via summaries, citations, or extracted insights
Inability to honor data minimization, retention limits, or subject rights requests
Increased blast radius when files are shared across agents or external tools
Elevated risk of data leakage through prompt injection or model inference
Violations of privacy-by-design, transparency, and record-keeping requirements
Attack Vector: File upload → parsing / OCR / document ingestion pipeline Attack Complexity: Low Privileges Required: None User Interaction: None, especially in automated ingestion or agent workflows. Confidentiality Impact: Critical Integrity Impact: Medium Availability Impact: Low
Frameworks
OWASP-LLM: LLM03, LLM05, LLM08
OWASP-ASI: ASI-01, ASI-05, ASI-09, ASI-12
EU AI Act: Articles 11, 12, 13, plus GDPR-aligned data-minimization and privacy-by-design principles embedded across the Act
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Uploaded files must not contain PII (Singapore) – Critical
AI_DAT_SEC_024
Violation Summary This applies to the information that is considered PII in Singapore. If files uploaded to an AI agent are ingested without redacting PII, sensitive data can be unintentionally exposed, propagated, retained, or disclosed through agent reasoning, LLM prompts, logs, embeddings, or downstream tool calls. Because AI agents often summarize, transform, store, and share file contents across systems, unredacted PII significantly amplifies privacy, compliance, and security risks and makes post-incident remediation extremely difficult.
Affected Assets
AI Agent
Technical Details
Failing to redact PII from files uploaded to AI agents introduces several risks including:
Exposure of sensitive personal data through agent outputs, summaries, or citations
Propagation of PII into prompts, vector databases, caches, logs, and telemetry
Uncontrolled sharing of PII with external LLM providers or third-party tools
Inability to comply with data minimization, retention limits, or deletion requests
Increased risk of data leakage via prompt injection or inference attacks
Broader blast radius when agents reuse or redistribute file content
Violations of privacy-by-design, transparency, and record-keeping requirements
Attack Vector: File upload → agent ingestion / parsing Attack Complexity: Low Privileges Required: None User Interaction: None (especially in automated or agent-driven workflows) Confidentiality Impact: Critical Integrity Impact: Medium Availability Impact: Low
Frameworks
OWASP LLM: LLM03, LLM05, LLM08
OWASP ASI: ASI-01, ASI-05, ASI-09, ASI-12
Singapore PDPA: Section 11, 13, 18, 24
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
No file should contain any PII – Critical
AI_DAT_SEC_025
Violation Summary If files contain unredacted personally identifiable information (PII), there is risk of sensitive data access to unrestricted access, misuse, and downstream propagation. This increases the likelihood of data leakage, unauthorized scraping, AI training contamination, and regulatory violations. Once exposed, PII can be copied, indexed, cached, or redistributed beyond the organization’s control.
Technical Details
Failing to redact PII from publicly accessible files introduces several risks including:
Unrestricted access to sensitive personal data by internal users, contractors, or the public
Mass data harvesting, scraping, or indexing by automated tools and AI systems
Propagation of PII into LLM prompts, embeddings, search indexes, and external datasets
Inability to enforce consent, purpose limitation, or access controls
Permanent exposure due to caching, backups, screenshots, or mirrors
Increased insider threat and accidental disclosure risk
Severe regulatory, legal, and reputational impact
Attack Vector: Public file access / shared repositories / collaboration platforms Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: Medium Availability Impact: Low
Frameworks
OWASP LLM: LLM03, LLM05, LLM08
OWASP ASI: ASI-01, ASI-05, ASI-09, ASI-12
EU AI Act: Article 11, Article 12, Article 13, Article 50, GDPR-aligned data minimization and privacy-by-design principles
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Identity and Access Control
Enforces authentication and least-privilege controls between AI agents, MCP servers, and endpoints.
MCP client must authenticate MCP server – Critical
AI_IAC_002
Violation Summary If an MCP client does not authenticate the MCP server, it cannot verify the identity, legitimacy, or trustworthiness of the system providing tool responses, commands, or data. This creates an opportunity for attackers to impersonate the MCP server, intercept or modify traffic, inject malicious tool responses, or deliver falsified data that influences downstream agent reasoning. Without authentication, the MCP trust boundary collapses, enabling man-in-the-middle attacks, data manipulation, workflow corruption, and full compromise of AI-driven operations.
Affected Assets
MCP Server
MCP Client
AI Agent
Technical Details Not authenticating the MCP server introduces several risks including:
Server impersonation leading to injection of malicious or misleading responses
Man-in-the-middle interception and modification of MCP traffic
Unauthorized access to sensitive MCP capabilities, tools, and agent operations
Corruption of workflows through falsified output or manipulated data
Leakage of PII or sensitive context exchanged with the illegitimate server
Loss of integrity, trust, and accountability in MCP-driven decisions
Violations of transparency, security, and traceability requirements
Attack Vector: Network/API communication between MCP client and server Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Prompt/Instruction Injection), LLM04 (Model Behavior Manipulation), LLM05 (Sensitive Information Disclosure), LLM08 (Insufficient Transparency & Auditability)
March 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-04 (Governance), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus robustness and security obligations for high-risk systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
MCP server must authenticate all clients – Critical
AI_IAC_006
Violation Summary If an MCP server does not authenticate the client making requests, any unauthorized entity—including compromised agents, external attackers, or untrusted processes—can impersonate a legitimate client. This allows attackers to invoke privileged tools, access sensitive data, manipulate workflows, or trigger system actions without detection. Lack of client authentication effectively removes all trust boundaries, enabling full compromise of server-side capabilities and AI-driven operations.
Affected Assets
LLM
AI Agent
Technical Details Not authenticating the MCP client introduces several risks including:
Unauthorized invocation of high-privilege tools or system actions
Full impersonation of trusted agents, enabling malicious or deceptive requests
Data exposure through unrestricted access to server responses, APIs, or internal systems
Manipulation of downstream workflows via falsified or maliciously crafted requests
Escalation of privilege or lateral movement across connected systems
Loss of accountability, traceability, and auditability for all client-driven actions
Violations of integrity, transparency, and security obligations for regulated AI systems
Attack Vector: Client → MCP server request path Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Framework (Compressed)
Nov 18, 2024 – OWASP-LLM: LLM01 (Instruction Injection), LLM04 (Behavior Manipulation), LLM05 (Sensitive Data Exposure), LLM08 (Transparency & Auditability Failures) Mar 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-04 (Governance), ASI-09 (Traceability), ASI-12 (Monitoring) Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus Annex III robustness & security requirements
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Inter-agent communication must be authenticated – Critical
AI_IAC_007
Violation Summary If agents communicate without authentication, any unauthorized party—including rogue agents, compromised services, or external attackers—can impersonate a legitimate agent and issue commands, request data, or alter system behavior. Non-authenticated inter-agent communication destroys trust boundaries between autonomous components and enables impersonation, privilege escalation, data leakage, workflow manipulation, and full compromise of multi-agent systems. Without identity guarantees, agent-to-agent messaging becomes a high-risk attack surface.
Affected Assets
AI Agent
Technical Details Missing authentication between agents introduces several risks including:
Unauthorized agents impersonating trusted components to issue commands
Manipulation of agent workflows or decision chains through falsified messages
Leakage of sensitive data exchanged during inter-agent coordination
Injection of malicious instructions into distributed reasoning processes
Loss of accountability and inability to attribute harmful actions
Increased lateral movement risk across agent networks
Violations of integrity, trust, and regulatory controls for autonomous systems
Attack Vector: Inter-agent message channel / network communication Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: Critical Availability Impact: Medium
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Instruction Injection), LLM04 (Behavior Manipulation), LLM05 (Sensitive Data Disclosure), LLM08 (Transparency & Audit Failures)
March 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-04 (Governance), ASI-07 (Reliability), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), plus Annex III security & robustness expectations for high-risk systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Agents must not hold excessive external system credentials – High/Critical
AI_IAC_008
Violation Summary When agents are configured with credentials to access more than three external systems, the blast radius of a compromise dramatically increases. Each additional credential expands the agent’s privilege footprint and creates new pathways for lateral movement, data exfiltration, unauthorized actions, and multi-system compromise. Over-privileged agents become single points of systemic failure if the agent is hijacked, attacked, misconfigured, or manipulated by an LLM prompt, all connected external systems are at risk simultaneously.
Affected Assets
AI Agent
Technical Details Allowing an agent to hold multiple (3+) external system credentials introduces several risks including:
Large blast radius: compromising the agent compromises all connected systems
Increased likelihood of credential leakage through logs, LLM outputs, prompts, or tool interactions
Prompt injection enabling unauthorized use of high-privilege multi-system access
Lateral movement across different platforms (e.g., Jira → GitHub → AWS → Snowflake)
Violation of least-privilege and separation-of-duties principles
Difficulty revoking or rotating credentials in incident response
Loss of governance and traceability when many systems are accessed through one agent identity
Attack Vector: Agent credential store / agent-initiated external API calls Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: Critical Integrity Impact: Critical Availability Impact: Medium to High
Framework
Nov 18, 2024 – OWASP-LLM: LLM01 (Injection), LLM04 (Behavior Manipulation), LLM05 (Sensitive Data Disclosure), LLM08 (Transparency & Audit Failures)
Mar 2025 – OWASP-ASI: ASI-01 (Integrity), ASI-04 (Governance), ASI-05 (Safe Handling), ASI-09 (Traceability), ASI-12 (Monitoring)
Aug 1, 2024 – EU AI Act: Articles 11 (Documentation), 12 (Record-Keeping), 13 (Transparency), 50 (Transparency Obligations), Annex III controls emphasizing least privilege, system robustness, and secure integration
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
LLM endpoints must require authentication – Critical
AI_IAC_009
Violation Summary If an LLM endpoint allows unauthenticated access, any external party can invoke the model without identity verification, usage controls, or accountability. This exposes the LLM to abuse, data leakage, prompt injection, denial-of-service, and unauthorized use of compute resources. In agentic and tool-enabled environments, unauthenticated access can also enable attackers to manipulate downstream workflows, trigger unsafe actions, or extract sensitive information without detection.
Affected Assets
LLM
Technical Details
Allowing unauthenticated access to an LLM endpoint introduces several risks including:
Unauthorized use of the model for malicious or abusive purposes
Prompt injection and adversarial inputs from unknown or untrusted actors
Leakage of sensitive context, system prompts, or embedded data
Abuse of compute resources leading to cost overruns or service degradation
Inability to enforce rate limits, quotas, or usage policies per identity
Loss of accountability, auditability, and forensic traceability
Circumvention of governance, approval, and access-control processes
Attack Vector: Public or unauthenticated API endpoint Attack Complexity: Low Privileges Required: None User Interaction: None Confidentiality Impact: High Integrity Impact: High to Critical Availability Impact: High (due to abuse or denial-of-service)
Frameworks
Nov 18, 2024 – OWASP-LLM: LLM0, LLM03 LLM04, LLM08 Mar 2025 – OWASP-ASI: ASI-01, ASI-04, ASI-09, ASI-12 Aug 1, 2024 – EU AI Act: Articles 11, 12, 13, 50 and obligations for secure access control and robustness in high-risk and GPAI systems
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://artificialintelligenceact.eu/ai-act-explorer/
Vulnerability
Continuously detects and remediates critical software weaknesses inside AI assets.
Do not allow dependencies with critical or high severity vulnerabilities – Critical
AI_VULN_SEC_001
Violation Summary Allowing AI agents to depend on libraries or packages with known critical or high-severity vulnerabilities introduces a direct and exploitable attack surface into the agent runtime. AI agents typically operate with elevated privileges, access sensitive data, invoke tools, and interact with external systems. Vulnerable dependencies can be exploited to execute arbitrary code, escalate privileges, leak secrets, poison agent behavior, or compromise downstream systems—often without direct interaction with the LLM itself.
Affected Assets
AI Agent
MCP Server
LLM
Technical Details
Using dependencies with critical or high vulnerabilities introduces several risks including:
Remote code execution or arbitrary command execution within the agent environment
Credential theft, token leakage, or exposure of secrets used by the agent
Supply-chain attacks where malicious code is introduced via compromised packages
Manipulation or poisoning of agent logic, tool invocation, or decision flows
Lateral movement across systems accessed by the agent
Persistence mechanisms established through compromised libraries
Inability to trust agent outputs or actions due to compromised runtime integrity
Violations of secure development, patch management, and risk-management obligations
Attack Vector: Vulnerable third-party dependency / supply-chain compromise Attack Complexity: Low Privileges Required: None (exploits often execute with agent privileges) User Interaction: None Confidentiality Impact: Critical Integrity Impact: Critical Availability Impact: Medium to High
Frameworks
OWASP LLM: LLM05, LLM08
OWASP ASI: ASI-05
NIST SSDF: Practice RV.1
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://csrc.nist.gov/pubs/sp/800/218/final
Do not allow critical or high vulnerabilities in the code – Critical
AI_VULN_SEC_002
Violation Summary The presence of vulnerabilities in application or agent code introduces direct security risks that can be exploited to compromise confidentiality, integrity, and availability. Vulnerable code paths—such as injection flaws, insecure deserialization, broken authentication, improper authorization, or unsafe file handling—can be abused by attackers to execute arbitrary code, access sensitive data, manipulate AI behavior, or disrupt operations. In AI and agent-based systems, these vulnerabilities are especially dangerous because compromised code can influence autonomous decisions and propagate impact across multiple systems.
Affected Assets
AI Agent
MCP Server
LLM
Technical Details
Having vulnerabilities in code introduces several risks including:
Remote code execution or command injection through exploitable code paths
Unauthorized access to sensitive data, credentials, or internal APIs
Manipulation of AI agent logic, reasoning flows, or tool invocation
Privilege escalation or bypass of authorization controls
Lateral movement across integrated systems and services
Persistence mechanisms established through exploited vulnerabilities
Loss of trust in application outputs and automated decisions
Violations of secure development lifecycle and regulatory requirements
Attack Vector: Vulnerable application or agent code Attack Complexity: Low to Medium (depending on vulnerability type) Privileges Required: None (for many common vulnerabilities) User Interaction: None or minimal Confidentiality Impact: Critical Integrity Impact: Critical Availability Impact: Medium to High
Frameworks
OWASP LLM: LLM05, LLM08
OWASP ASI: ASI-05
NIST SSDF: Practice RV.2
References
https://genai.owasp.org/llm-top-10/ https://genai.owasp.org/initiatives/agentic-security-initiative/ https://csrc.nist.gov/pubs/sp/800/218/final
Last updated