Modern AI applications and autonomous agents frequently consume information from external websites, APIs, documentation portals, knowledge bases, Git repositories, and other third-party resources. As in most cases, organizations do not control these third party sources, it is not possible to identify if content is modified. In fact, in most cases, these contents are dynamically generated. When AI agents are consuming these data, it becomes important to validate data.
Our AI Agent Trap Assessment service identifies whether AI agents can be manipulated through trusted external content. Instead of attacking the AI platform directly, we simulate malicious third-party resources and validate whether the agent follows hidden instructions, leaks sensitive information, performs unauthorized actions, or consumes excessive resources.
01
Business & Agent Understanding
- AI agents in scope
- External websites, APIs and knowledge sources consumed
- RAG data sources
- Third-party documentation or content repositories
- Business workflows triggered by external content
- Sensitive actions performed by the agent
The assessment begins by understanding how the AI implementation works. During this phase, we identify:
02
External Content Mapping
- Public websites
- Documentation portals
- Git repositories
- APIs
- RSS feeds
- Knowledge bases
- Shared documents
- Other third-party content sources
We map every external resource that can influence agent behaviour. This includes:
The objective is to identify trust boundaries where external content can influence AI decisions.
03
Agent Trap Simulation & Behviour Validation
- Indirect prompt injection/hidden instructions
- Tool misuse
- Sensitive data extraction attempts
- Unauthorized workflow execution
- Resource consumption attacks
We host controlled test content on our own infrastructure and requests the organization to temporarily configure the AI agent to consume this content instead of the original source. The hosted content contains carefully designed hidden instructions that simulate real-world attacks such as:
Since the testing is performed using our controlled environment, no third-party resources are modified during the assessment. The AI agent is observed while processing the controlled content to validate whether the agent executes hidden instructions, ignores system prompts, leaks sensitive information, performs unintended actions etc. Evidence is collected for every successful or partially successful trap.
04
Risk Assessment & Reporting
- Each successful agent trap is documented with required evidence of the attack scenario, vulnerable data source, hidden instructions, agent behaviour, exploit & business impact, recommendation. The report provides clear guidance on protecting AI agents from malicious third-party content and improving trust validation before external information is consumed.