AI & LLM Security
Training

Stop a hidden prompt from hijacking your AI assistant mid-task.

• Detect hidden instructions embedded in documents processed by AI
• Trace how injected prompts override legitimate AI behavior
• Apply safe document handling before feeding content to AI tools

See what happens when confidential data enters a consumer AI tool.

• Recognize sensitive data categories that should never enter AI prompts
• Trace how pasted content persists in AI training data and logs
• Apply data classification policies before using AI tools

Deploy an AI plugin that hides a backdoor in plain sight.

• Identify supply chain risks in third-party AI models and plugins
• Detect behavioral anomalies in AI components from external sources
• Apply vetting procedures before deploying marketplace AI tools

Watch poisoned documents corrupt your AI's answers in real time.

• Trace how manipulated documents alter AI-generated outputs
• Identify signs of data poisoning in AI responses
• Apply content integrity controls to knowledge base inputs

Exploit an AI whose outputs flow unchecked into live systems.

• Identify injection risks when AI outputs feed into downstream systems
• Trace how unsanitized AI output enables code execution
• Apply output validation controls between AI and connected systems

Manipulate an AI assistant into misusing its own permissions.

• Identify excessive permissions granted to AI agents
• Trace unauthorized actions performed by manipulated AI tools
• Apply least-privilege principles to AI agent configurations

Extract hidden instructions from a customer-facing AI chatbot.

• Execute prompt extraction techniques against a live AI chatbot
• Identify sensitive information exposed through leaked system prompts
• Apply prompt hardening techniques to prevent system instruction disclosure

Exploit a RAG pipeline to access documents beyond your clearance.

• Identify access control failures in vector database retrieval
• Trace how adversarial embeddings corrupt search results
• Apply authorization checks at the retrieval layer of RAG systems

Catch fabricated statistics and fake citations in an AI report.

• Detect hallucinated facts and fabricated sources in AI outputs
• Verify AI-generated claims against authoritative references
• Apply fact-checking workflows to AI-assisted business content

Launch a denial-of-wallet attack against an unprotected AI API.

• Identify resource exhaustion vectors in AI API endpoints
• Trace how crafted prompts escalate compute costs exponentially
• Apply rate limiting and budget controls to AI service deployments

Stop an autonomous AI agent from being redirected by a poisoned email containing hidden instructions.

• Detect hidden instructions embedded in incoming data that redirect agent objectives
• Trace how a goal-hijacked agent pivots from legitimate tasks to data exfiltration
• Apply input validation strategies that prevent agents from treating data as instructions

Prevent an AI agent from being manipulated into using its legitimate tools to delete files and send unauthorized messages.

• Identify how ambiguous prompts cause agents to misuse legitimate tool access
• Trace destructive tool calls triggered by manipulated input parameters
• Apply least-privilege tool access policies to contain agent tool exploitation

Prevent an AI agent from reusing inherited high-privilege credentials to access systems beyond its authorized scope.

• Trace how agents inherit and propagate user credentials across different system contexts
• Identify confused deputy vulnerabilities where agent privilege exceeds intended scope
• Apply scoped credential delegation to prevent cross-context privilege escalation

Investigate a backdoored third-party AI plugin that silently modifies agent behavior and exfiltrates sensitive data.

• Detect behavioral anomalies indicating a compromised third-party AI component
• Trace data exfiltration pathways through backdoored plugins and MCP servers
• Apply supply chain verification practices before integrating external AI tools

Catch an AI coding assistant before it executes a shell script containing injected commands that compromise your system.

• Detect injected commands hidden within AI-generated code and shell scripts
• Trace how user input flows through code generation into unsandboxed execution
• Apply code review and sandboxing practices to AI-generated scripts before execution

Detect adversarial content injected into an AI agent's persistent memory that corrupts all future decisions.

• Identify poisoned entries in an agent's persistent memory and retrieval context
• Trace how corrupted memory influences downstream agent decisions across sessions
• Apply memory integrity verification to detect and remove adversarial content

Intercept and identify spoofed messages between AI agents in a multi-agent workflow before fabricated instructions cause damage.

• Detect spoofed agent identities and fabricated messages in multi-agent communication channels
• Trace how unauthenticated inter-agent messages enable man-in-the-middle attacks
• Apply message authentication and agent identity verification to secure multi-agent systems

Contain a minor AI hallucination before it cascades through downstream agents into a catastrophic system-wide failure.

• Trace error propagation from a single agent hallucination through multiple downstream systems
• Identify amplification points where small errors compound into catastrophic outcomes
• Apply circuit breaker patterns and human checkpoints to interrupt cascading agent failures

Catch a series of compromised AI agent recommendations that exploit your trust to approve a fraudulent transfer and a backdoored code change.

• Recognize automation bias patterns where consistent AI accuracy creates false confidence
• Identify subtle anomalies in AI recommendations that indicate manipulation or compromise
• Apply structured verification workflows that resist social engineering through AI interfaces

Investigate a compromised AI agent that appears functional while silently performing unauthorized actions and evading monitoring.

• Detect covert unauthorized actions performed by an agent that appears to be operating normally
• Trace persistence mechanisms that allow rogue agents to survive restarts and monitoring sweeps
• Apply behavioral analysis and anomaly detection to distinguish rogue agents from legitimate ones