AI Identity Spoofing

AI Identity Spoofing is an attack technique in which adversaries manipulate an AI system into accepting false identities, impersonating trusted users, or acting upon forged contextual information. Successful attacks may bypass authorization decisions, expose sensitive information, execute privileged workflows, or compromise enterprise trust relationships.

AI Identity Spoofing targets the trust relationships that exist between AI systems, users, enterprise applications, and connected services. Rather than compromising authentication mechanisms directly, attackers manipulate contextual information, prompts, conversation history, workflow state, or identity assertions to convince AI systems that they are interacting with an authorized user or trusted process.

Modern AI assistants frequently operate within authenticated enterprise environments where user identity influences access decisions, retrieved information, workflow execution, and connected tool permissions. If identity assumptions can be manipulated, attackers may gain access to privileged information or influence AI-driven business processes without compromising traditional authentication controls.

Identity spoofing may be combined with prompt injection, workflow manipulation, memory poisoning, or retrieval attacks to increase the effectiveness of follow-on exploitation. Organizations implementing AI agents with delegated authority or role-based access controls face particularly elevated risk.

Redbot Labs classifies AI Identity Spoofing as a Critical AI attack because it undermines the trust boundaries that govern enterprise AI authorization decisions.

AI Identity Spoofing generally follows a structured progression from reconnaissance through unauthorized access.

1. Target Identification

The attacker identifies AI systems that make authorization or workflow decisions based upon conversational identity or contextual trust.

2. Identity Reconnaissance

The attacker gathers information about user roles, permissions, workflows, and organizational structure.

3. Identity Manipulation

Prompts, contextual information, memory, or workflow state are manipulated to establish a false identity or trusted relationship.

4. Authorization Influence

The AI accepts the manipulated identity and alters responses, retrieval behavior, or workflow execution accordingly.

5. Privileged Access

The attacker gains access to information, tools, or business functions beyond intended authorization.

6. Validation

The attacker confirms successful impersonation through additional interactions or operational results.

7. Enterprise Impact

Unauthorized access compromises sensitive information, business workflows, or connected enterprise systems.

AI Identity Spoofing primarily affects enterprise AI systems that make authorization decisions based on user identity, role assignments, conversational context, or delegated permissions. Organizations deploying AI assistants with access to sensitive business data, internal workflows, or privileged enterprise systems face elevated risk because manipulated identity context can influence both information retrieval and operational behavior.

Commonly affected systems include:

• Enterprise AI assistants
• Customer service AI platforms
• AI copilots
• Retrieval-Augmented Generation (RAG) systems
• AI workflow automation platforms
• Identity-aware AI applications
• Security Operations AI assistants
• IT Operations AI assistants
• Human Resources AI systems
• Financial AI platforms
• Healthcare AI assistants
• Legal AI assistants
• CRM platforms
• ERP applications
• Knowledge management systems
• AI-enabled help desks
• Multi-agent platforms
• Autonomous AI agents
• Cloud AI services
• Enterprise collaboration platforms

High-Risk Characteristics

Risk increases significantly when AI systems:

• Make authorization decisions using conversational context
• Access role-based enterprise information
• Execute privileged workflows
• Retrieve identity-sensitive information
• Integrate with IAM platforms
• Perform delegated actions
• Access regulated business data
• Maintain persistent user context
• Support administrative functions
• Execute actions without independent authorization validation

AI Identity Spoofing undermines trust in enterprise AI by manipulating how systems interpret identity, authorization, and delegated permissions. Successful attacks can enable unauthorized access to sensitive information, privileged workflows, and connected enterprise resources without directly compromising traditional authentication mechanisms.

Potential business impacts include:

• Unauthorized disclosure of sensitive information
• Privilege escalation
• Impersonation of trusted users
• Workflow manipulation
• Unauthorized business transactions
• Identity and access control failures
• Regulatory and compliance violations
• Operational disruption
• Loss of trust in AI-driven decision making
• Reputational damage

Organizations should treat identity validation as a fundamental security boundary and ensure that AI systems cannot make authorization decisions based solely on conversational context or inferred user intent.

Redbot Labs evaluates AI Identity Spoofing through structured adversarial testing designed to determine whether AI systems can be manipulated into accepting false identities, unauthorized roles, or forged trust relationships.

Manual validation activities include:

• Identity context manipulation
• Role impersonation testing
• Delegated authority validation
• Session context assessment
• Authorization boundary testing
• AI trust relationship validation
• Workflow ownership testing
• Multi-user context evaluation
• Prompt-based impersonation assessment
• Identity persistence testing
• API authorization validation
• Agent trust boundary assessment
• Enterprise role verification
• Operational authorization testing

Successful findings are manually reproduced to verify exploitability, authorization failures, and business impact before reporting.

Organizations should monitor AI systems for unauthorized identity assumptions, abnormal authorization decisions, or AI actions performed under manipulated user context.

Indicators include:

• Unexpected role changes
• Unauthorized identity assumptions
• Access to information outside assigned permissions
• AI responses inconsistent with authenticated user privileges
• Unexpected delegated actions
• Abnormal workflow ownership
• Identity context anomalies
• Cross-user information exposure
• Suspicious authorization events
• Unexplained privilege changes

Organizations should log authentication context, authorization decisions, AI prompts, and workflow execution to improve detection and forensic investigation.

MITRE ATLAS AML.T0051 Prompt Injection MITRE ATT&CK T1078 Valid Accounts MITRE ATT&CK T1556 Modify Authentication Process

OWASP LLM02:2025 Sensitive Information Disclosure OWASP LLM08:2025 Excessive Agency