Redbot Security’s AI security research hub covers LLM testing, prompt injection, RAG risk, AI agents, data leakage, model abuse, tool invocation, and the attack paths created when generative AI connects to real business systems.
As organizations deploy copilots, AI-enabled workflows, retrieval-augmented generation, and autonomous agents, the security boundary is no longer just the web application. It includes prompts, context windows, retrieval sources, APIs, tools, permissions, identity paths, cloud data, and human approval workflows.
AI security testing requires a broader view than traditional application testing. Security teams need to understand how model behavior, user-controlled prompts, retrieved data, API-connected tools, and agent workflows can be abused together.
Research on malicious instructions, indirect prompt injection, instruction hierarchy abuse, jailbreak attempts, and model-facing input manipulation.
Testing retrieval-augmented generation for data leakage, poisoned content, unauthorized retrieval, context exposure, and trust-boundary failure.
Validation of tool invocation, permission boundaries, autonomous actions, workflow abuse, API access, and agent-connected business logic.
Analysis of sensitive data exposure through prompts, training context, retrieval systems, logs, chat history, API keys, and connected workflows.
These guides help security leaders, application teams, and AI builders understand how AI-enabled systems fail, what attackers target, and how to validate AI workflows before they create real exposure.
AI Threats
How coordinated autonomous agents can compress attack timelines, operate in parallel, and reshape offensive security risk.
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Prompt Injection
A practical look at prompt injection exploitation patterns and how security teams should validate AI applications beyond basic guardrails.
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LLM Testing
How to test prompt injection, model exposure, workflow abuse, and hidden trust assumptions inside enterprise AI deployments.
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Data Leakage
Where model memory, retrieval, prompts, and workflow trust boundaries create sensitive data exposure in modern AI systems.
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AI Validation
Why AI security testing goes beyond traditional app testing to pressure-test model behavior, unsafe outputs, and integration abuse.
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RAG Security
Why enterprise RAG workflows need adversarial testing for retrieval trust, context poisoning, leakage, and unsafe decisions.
Read Article →Traditional application security testing usually focuses on application code, authentication, authorization, session handling, API behavior, business logic, and infrastructure exposure. AI security testing includes those concerns, but adds model-connected risks that behave differently from standard software vulnerabilities.
In AI-enabled applications, a user may influence model behavior through natural language. The model may retrieve documents, call tools, invoke APIs, summarize sensitive content, trigger workflows, or produce outputs that affect real decisions. That means AI testing must validate both the application layer and the model-connected workflow around it.
Organizations building or adopting AI systems should test the full chain of risk: model-facing inputs, retrieval systems, identity permissions, APIs, tools, cloud data, logs, approval flows, and human-in-the-loop controls.
Validate whether malicious instructions can override system prompts, manipulate model behavior, or expose hidden context.
Test whether retrieval workflows expose unauthorized documents, poisoned data, sensitive snippets, or privileged context.
Assess whether AI agents can trigger tools, APIs, transactions, messages, or automations beyond intended boundaries.
Verify that AI workflows respect user roles, tenant isolation, object-level permissions, and data access rules.
Review prompts, logs, chat histories, embeddings, retrieval sources, API responses, and generated outputs for sensitive data exposure.
Test whether model output can influence business workflows, approvals, support actions, routing, or customer-facing decisions unsafely.
Validate how AI systems interact with APIs, SaaS platforms, cloud storage, IAM, databases, and privileged services.
Determine whether prompt abuse, tool misuse, retrieval anomalies, and unsafe AI behavior would be detected and investigated.
Review where human review, approval, escalation, and rollback controls are needed before AI actions affect production workflows.
Redbot Security helps organizations validate prompt injection exposure, RAG security, AI data leakage, tool abuse, agent workflows, authorization boundaries, and model-connected application risk before attackers find those paths first.
