AI & LLM Security
Training

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