A Deep Dive into Preventing Insecure Output Handling
LLM outputs can leak data, enable misuse, or trigger downstream risks if they are not explicitly constrained, filtered, and evaluated across interfaces.
To win, your GenAI solutions must control, filter, and validate outputs with the same rigor applied to inputs and access.
When output handling is treated as an afterthought, GenAI risks surface in subtle but damaging ways.
• Unclear misuse risks: Teams lack a shared understanding of how generated outputs can be misused or leak sensitive information.
• Unsafe output patterns: Dangerous output types and triggers are not consistently identified or mitigated.
• Weak interface controls: APIs and user interfaces pass through risky outputs without sufficient evaluation or filtering.
These gaps lead to data leakage, unsafe downstream behavior, and increased exposure across integrated systems.
In this hands-on workshop, your team designs and evaluates secure output handling strategies through structured analysis and applied exercises.
• Define risks associated with output misuse and unintended data leakage.
• Identify dangerous output types and triggers across common GenAI use cases.
• Embed post-generation output filters to enforce safety and policy constraints.
• Analyze output patterns that enable data extraction or misuse and design mitigations.
• Evaluate output handling behaviors across APIs and user interfaces.
Defining Risks of Output Misuse and Leakage
Identifying Dangerous Output Types and Triggers
Embedding Post-Generation Output Filters
Preventing Data Extraction Through Output Patterns
Evaluating Output Handling in APIs and Interfaces
• Recognize how GenAI outputs can introduce security and data risks.
• Identify high-risk output types and triggering conditions.
• Apply post-generation filtering techniques to constrain unsafe outputs.
• Reduce data extraction risk through safer output patterns.
• Leave with clear criteria for evaluating output handling across interfaces.
Who Should Attend:
Solution Essentials
Virtual or in-person
4 hours
Intermediate
Output examples, filtering patterns, and guided evaluation exercises