Network Penetration Testing
THE ENTERPRISE AI ATTACK STACK

Enterprise AI & LLM Security Testing.

Identify vulnerabilities across AI models, prompts, RAG pipelines, agentic workflows, MCP integrations, enterprise data, APIs, and AI-enabled applications through expert-led manual security testing.

DISCUSS YOUR AI EXPLORE CAPABILITIES
AI Systems
Agents, models, data exposure
Applications
Apps, APIs, logic flaws
Cloud
Configs, trust paths, exposure
Network
Internal & external surface
Red Team
Adversary simulation
THE ENTERPRISE AI ATTACK STACK

Manual Security Testing Across the Entire AI Attack Stack

Redbot Security validates how attackers can manipulate AI applications, LLM workflows, RAG systems, AI agents, APIs, cloud integrations, and connected business processes to expose data, bypass controls, misuse tools, or trigger unintended actions.

ADVERSARIAL AI TESTING

Prompt Injection And Behavior Manipulation

Redbot tests whether direct or indirect instructions can manipulate AI behavior, override intended controls, expose sensitive data, or influence downstream actions.

RAG SECURITY

Retrieval, Context, And Data Exposure

We evaluate retrieval-augmented generation systems, knowledge sources, vector databases, document access, context poisoning, and sensitive data exposure risks.

AGENT SECURITY

Tool Use, APIs, And Workflow Abuse

Redbot validates whether AI agents, plugins, APIs, tool calls, and workflow automation can be abused to access restricted data or trigger unintended business actions.

CONNECTED ENVIRONMENTS

Application, API, Cloud, And Identity Trust

AI systems often inherit trust from surrounding applications, APIs, cloud services, identity systems, and internal workflows. Redbot tests the complete connected exposure.

ASSURANCE AND DELIVERY MODEL

Redbot Security is ISO 27001:2022 certified and maintains SOC 2 Type I and Type II assurance. Our senior-led reporting supports remediation planning, governance review, vendor assurance, HIPAA-driven environments, and GDPR-driven environments.

ISO 27001:2022 SOC 2 Type I SOC 2 Type II OSCP CRTO GPEN CISSP CCSP CCSK AWS HIPAA Support GDPR Support
HOW AI SYSTEMS GET COMPROMISED

AI Compromise Extends Across the Entire Attack Stack

Redbot tests every connected layer, including the model, application logic, retrieval systems, APIs, cloud services, tools, agents, identity controls, and business workflows.

Attackers do not need to break the model directly. They can exploit prompt handling, poisoned retrieval, exposed integrations, excessive permissions, weak authorization, trusted automation, and downstream workflows connected to the AI system.

PROMPT INJECTION

Instruction Manipulation

Attackers may manipulate direct or indirect prompts to override intended behavior, bypass guardrails, influence model output, expose data, or abuse downstream trust.

RAG SECURITY

Retrieval And Context Poisoning

Manipulated documents, unsafe knowledge sources, weak retrieval controls, poisoned context, or excessive document access can alter outputs or expose sensitive information.

TOOL AND API ABUSE

Unsafe Function Calls

APIs, plugins, agents, and orchestration layers may allow AI systems to access restricted data, call sensitive functions, or trigger actions beyond intended limits.

AGENTIC WORKFLOWS

Automation And Workflow Abuse

AI agents connected to business workflows may inherit excessive trust, automate unsafe actions, expose sensitive systems, or create new paths to operational compromise.

IDENTITY AND ACCESS

Authorization Boundary Failure

AI applications can expose data or actions when user roles, session context, API permissions, tenant boundaries, or identity-backed access controls are not enforced correctly.

CLOUD AND DATA TRUST

Connected System Exposure

AI systems often connect to cloud storage, internal applications, databases, SaaS platforms, and APIs. Redbot tests whether those trust paths expose sensitive data or actions.

AI SECURITY TESTING OUTCOME

Redbot helps determine how an AI system can be manipulated, what sensitive data or actions are exposed, which controls fail under adversarial pressure, and what should be fixed first.

Redbot Security AI and LLM security testing visualization showing connected AI exposure, prompt injection risk, retrieval abuse, tool and API misuse, agentic workflows, and enterprise AI attack paths
AI EXPOSURE IS EVOLVING

AI Systems Are Already Connected. Have You Validated the True Exposure?

We test the full AI system, not just the model.

AI tools increasingly influence enterprise workflows, operational decisions, internal systems, APIs, and sensitive business data. Redbot identifies exploitable attack paths affecting connected environments, integrations, retrieval pipelines, agentic workflows, and inherited trust relationships before attackers uncover them first.

Prompt Injection
Retrieval Poisoning
Tool & API Abuse
Connected System Exposure
Core Operator Credentials
OSCP
CRTO
GPEN
CISSP
CCSP
CCSK
SecurityX
AWS
AI & MODERN ATTACK SURFACES

Redbot operators actively pursue modern offensive security training focused on AI systems, LLM exploitation, adversarial testing methodologies, cloud attack surfaces, and evolving offensive tradecraft.

PAPA AI Red Teaming HTB AI Path Adversarial AI
Compliance & Assurance
SOC 2 Type II assurance SOC 2 Type II
SOC 2 Type I assurance SOC 2 Type I
ISO 27001:2022 certification ISO 27001:2022
GDPR support GDPR Support
HIPAA support HIPAA Support
SSCP · PenTest+ · CySA+ · CEH · eMAPT · eJPT · Network+ · Security+ · Project+ · ITIL · Physical Security Testing · PCI-DSS-driven environments · Mobile Application Testing · Cloud Infrastructure Security
ASSESSMENT OUTCOMES

Redbot Validates AI Risk Across Models, Applications, Data, Tools, And Workflows

We test the full AI system, not just the model.

Redbot delivers manually validated findings, attack-path visibility, operational risk analysis, proof-of-concept evidence, and remediation guidance across connected AI environments, LLM workflows, RAG systems, agents, APIs, cloud services, and business processes.

FINDINGS

Manually Validated AI Exposure

Every finding is manually verified to identify realistic attack paths affecting AI applications, LLM workflows, prompt handling, retrieval pipelines, integrations, APIs, agents, and operational trust relationships.

RISK ANALYSIS

Operational Impact Visibility

Redbot maps how exploitable AI exposure may affect sensitive business workflows, internal systems, customer data, enterprise applications, cloud services, and connected infrastructure.

REMEDIATION

Actionable Security Guidance

Findings include prioritized remediation guidance designed to reduce exposure across prompts, retrieval systems, agent permissions, tool calls, APIs, workflows, and connected environments.

REPORTING

Executive And Technical Reporting

Redbot delivers clear reporting for technical teams and leadership stakeholders who need visibility into AI-related risk, proof-of-concept evidence, business impact, and remediation priorities.

AI ATTACK PATH COVERAGE

Follow The AI Attack Path From Manipulation To Business Impact

AI and LLM security testing is not just prompt testing. Redbot evaluates how instructions, retrieval systems, agents, APIs, cloud services, permissions, and enterprise workflows can combine into exploitable attack paths.

ENTRY VECTOR Prompt Injection Direct and indirect instruction manipulation BEHAVIOR CONTROL Jailbreak Testing Guardrail bypass and unsafe model behavior MODEL OUTPUT Behavioral Manipulation Response steering, unsafe output, and trust abuse
REDBOT AI TESTING MODEL

From AI Manipulation To Verified Exposure

Redbot tests how AI systems behave when attackers pressure the full environment, including prompts, retrieval sources, memory, agent tools, APIs, cloud services, identity boundaries, and workflow automation.

01 Manipulate
02 Retrieve
03 Execute
04 Impact
DATA LAYER RAG Security Retrieval abuse, context poisoning, and data exposure ACTION LAYER Agent Security Tool use, function calls, automation, and workflow abuse TRUST LAYER Cloud AI Workflows Cloud services, identity permissions, and connected trust paths
CONNECTED AI RISK AREAS

Use this section as a navigation layer for the page. It reinforces topical depth while keeping visitors connected to Redbot’s core AI, cloud, API, and advanced security services.

Memory Exposure AI data leakage and session risk Vector Database Risk Embedding, retrieval, and document access risk Context Poisoning Unsafe knowledge source manipulation AI API Trust Backend authorization and function exposure Operational Trust Advanced attack path and red team context AI Integration Risk Application, API, and workflow exposure
AI SECURITY FAQ

AI Security Questions Worth Asking Before Exposure Becomes Risk

We test the full AI system, not just the model.

AI systems are now connected to real users, sensitive data, APIs, cloud services, retrieval pipelines, agents, and business workflows. These questions explain where AI security testing fits, what Redbot validates, and why AI exposure should be assessed alongside application, API, cloud, and offensive security programs.

What is AI security testing?
AI security testing is an adversarial security assessment of AI-enabled applications, LLM workflows, RAG systems, AI agents, APIs, cloud integrations, data access controls, and connected business processes. The goal is to determine whether attackers can manipulate AI behavior, expose sensitive data, bypass authorization, abuse tools, or trigger unintended actions.
What is LLM security testing?
LLM security testing evaluates large language model applications for risks such as prompt injection, indirect prompt injection, system prompt exposure, sensitive data disclosure, insecure output handling, authorization bypass, unsafe tool use, jailbreak behavior, and excessive agency across connected systems.
Does AI security testing replace penetration testing?
No. AI security testing does not replace traditional penetration testing. It should sit alongside application, API, cloud, network, and red team assessments when AI systems are connected to real users, sensitive data, business workflows, operational tools, or enterprise infrastructure.
Why do AI applications need security testing?
AI applications need security testing because they often connect models to internal documents, APIs, cloud platforms, user data, third-party tools, and automated workflows. If those systems are not validated, attackers may manipulate prompts, abuse retrieval, expose sensitive data, bypass controls, or trigger actions the business never intended.
What does Redbot test during an AI security assessment?
Redbot tests the full AI system, including LLM applications, prompt and instruction layers, RAG pipelines, vector databases, AI agents, APIs, plugins, cloud integrations, authentication boundaries, data access controls, logging behavior, workflow automation, and operational trust relationships.
What is prompt injection testing?
Prompt injection testing evaluates whether attackers can manipulate AI instructions, override system behavior, bypass operational controls, leak sensitive data, influence downstream workflows, abuse tools, or cause the AI system to trust malicious user-supplied or third-party content.
What is indirect prompt injection?
Indirect prompt injection occurs when malicious instructions are hidden inside content the AI system retrieves or processes, such as documents, websites, emails, tickets, knowledge base articles, or tool outputs. Redbot tests whether retrieved or external content can manipulate the AI system or influence downstream actions.
What is RAG security testing?
RAG security testing validates retrieval-augmented generation systems connected to enterprise knowledge sources, vector databases, document stores, internal search systems, and operational data. Redbot tests whether retrieval workflows can be poisoned, manipulated, over-permissioned, or abused to expose sensitive information.
What is AI agent security testing?
AI agent security testing evaluates whether autonomous or semi-autonomous agents can be manipulated into misusing tools, calling unauthorized APIs, accessing restricted data, triggering unsafe workflows, escalating operational impact, or taking actions outside intended business rules.
What is AI data leakage?
AI data leakage occurs when sensitive information is exposed through model responses, prompts, logs, memory, retrieval results, embeddings, vector databases, API responses, connected tools, or cross-user access failures. Redbot tests how AI systems handle sensitive data across the full environment surrounding the model.
What is the difference between AI red teaming and AI penetration testing?
AI penetration testing focuses on identifying and validating exploitable technical weaknesses affecting AI systems and connected infrastructure. AI red teaming expands the scope to simulate realistic adversarial behavior, operational abuse, workflow manipulation, social engineering paths, and broader enterprise attack scenarios involving AI-enabled systems.
Does AI security testing include APIs, plugins, and cloud integrations?
Yes. Modern AI systems often depend on APIs, plugins, tool calls, cloud services, orchestration platforms, external data sources, SaaS integrations, and third-party workflows. Redbot validates whether connected infrastructure can be abused through the AI system or used to expand the impact of an AI-specific weakness.
Why is manual AI security testing important?
Manual AI security testing is important because automated scanners cannot reliably understand business logic, authorization boundaries, workflow abuse, adversarial reasoning, tool misuse, or multi-step attack chains. Redbot performs senior-led manual validation focused on realistic exploitation paths and proof-of-concept evidence.

Need to validate an AI application, LLM workflow, RAG system, or agent before exposure becomes business risk?

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RELATED SECURITY SERVICES

Additional Adversarial Security Validation Services.

RED TEAM

Adversarial simulation designed to identify exploitable operational attack paths affecting enterprise infrastructure.

CLOUD SECURITY

Cloud penetration testing and security validation across connected enterprise environments and exposed infrastructure.

NETWORK TESTING

Internal and external penetration testing focused on real-world network exposure and enterprise attack surface validation.

APPLICATION SECURITY

Web application security testing focused on exploitable vulnerabilities, authentication flaws, and business logic abuse.

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ADVERSARIAL VALIDATION

Validate your true AI exposure across enterprise systems, workflows, APIs, retrieval pipelines, and operational trust relationships.

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