Validate Real AI Attack Paths
Senior-led AI security testing focused on prompt injection, data leakage, unsafe tool use, agent workflow abuse, and the attack paths adversaries actually use against production AI systems.
Redbot evaluates prompts, models, context handling, retrieval layers, integrations, agents, permissions, and connected workflows to determine whether attackers can manipulate behavior, expose sensitive data, bypass safeguards, trigger unintended actions, or chain AI weaknesses into meaningful business impact.
What Is AI & LLM Security Testing?
AI and Large Language Model (LLM) security testing is the process of identifying vulnerabilities in AI-driven systems, including prompt injection, data leakage, model manipulation, insecure integrations, and unsafe or unintended outputs.
Unlike traditional application security testing, AI security focuses on how models interpret inputs, generate responses, handle context, and interact with connected tools, APIs, and internal data sources. These systems create a different attack surface, where subtle abuse can turn into meaningful security, privacy, and business risk.
Redbot Security simulates real-world adversarial behavior against AI systems to uncover exploitable weaknesses before they can be weaponized. Our testing evaluates how resilient your AI implementation is when exposed to malicious prompts, manipulated inputs, system abuse, and unsafe trust boundaries.
As organizations push AI into production, risk extends beyond code and infrastructure into model behavior, data exposure, orchestration logic, and decision pathways. Our methodology is built to assess these risks across both internal and customer-facing AI deployments.
Where AI Security Actually Breaks Down
AI systems introduce security risks that traditional testing often misses. When large language models connect to internal data, tools, workflows, and decision logic, attackers gain new ways to manipulate outputs, expose sensitive information, and abuse trusted systems at scale.
Prompt Injection Alters System Behavior
Attackers can override instructions, redirect workflows, and push models into unsafe actions using carefully crafted prompts. That creates exploitable risk even when the surrounding application appears secure.
Data Leakage Happens Through Interaction
Sensitive information can leak through prompts, context windows, retrieval pipelines, and connected data sources when AI systems are not properly segmented, filtered, and adversarially tested.
Integrations Expand the Blast Radius
When AI tools connect to APIs, plugins, internal systems, or business workflows, a single abuse path can become a route into broader operations, permissions, and trusted environments.
Unsafe Outputs Create Business Risk
AI-generated content can influence decisions, trigger actions, and shape user experiences. Without adversarial testing, organizations may rely on systems that can be manipulated, misled, or weaponized.
Modern AI Risk Goes Beyond The Model
AI security is not just about whether a model responds correctly. It is about how attackers abuse context, permissions, connected systems, and trust boundaries to create real operational impact.
Redbot evaluates AI systems with an attacker mindset, focusing on how small weaknesses become meaningful exploit paths across production environments.
How Redbot Tests AI & LLM Security
AI security testing goes beyond simple prompt experimentation. Redbot evaluates how attackers manipulate model behavior, abuse integrations, and exploit trust boundaries to create real-world impact across AI-enabled environments.
Prompt Injection & Instruction Override
We test how attackers manipulate inputs to override system instructions, bypass safeguards, and force models into unintended or unsafe behavior.
Data Exposure & Context Leakage
We evaluate how sensitive data can be extracted through prompts, context windows, retrieval pipelines, and improperly scoped knowledge integrations.
Guardrail & Safety Bypass Testing
We identify weaknesses in model safeguards and policy enforcement that allow restricted outputs, unsafe responses, or compliance failures.
Tooling & Integration Abuse
We test how AI systems interact with APIs, plugins, and internal tools to identify abuse paths that extend beyond the model into real system actions.
Output Manipulation & Decision Influence
We assess how attackers influence model outputs to alter decisions, recommendations, or user-facing responses in ways that create business risk.
Multi-Step & Chained Exploitation
We simulate advanced attack paths where multiple weaknesses are combined to escalate impact across workflows, permissions, and connected systems.
AI Risk Is More Than A Model Problem
Effective AI security testing looks beyond what a model says and focuses on what an attacker can make the broader system do. Redbot evaluates the real exploitation paths that affect data, applications, workflows, and business operations.
Why Organizations Choose Redbot for AI Security Testing
AI security testing requires more than surface-level validation or generic model checks. Redbot approaches AI and LLM security with an adversarial mindset, focusing on how real attackers manipulate models, abuse integrations, and turn weak controls into measurable business risk.
Hands-On Adversarial Testing
We simulate realistic abuse cases against AI systems, including prompt injection, context manipulation, unsafe tool usage, and multi-step attack paths that automated checks often miss.
Proof-of-Concept Validation
Redbot does not stop at theory. We validate exposure through practical testing and demonstrate how weaknesses can be exploited in ways that matter to security teams, stakeholders, and system owners.
Beyond the Model Itself
AI risk does not live only in the model. We assess the surrounding ecosystem, including integrations, permissions, retrieval workflows, data access paths, and operational trust boundaries.
Actionable Remediation Guidance
Findings are translated into practical recommendations your team can act on. We focus on reducing exploitable risk, improving controls, and helping teams harden AI systems for real-world use.
Custom Scoping for Real Environments
Every AI deployment is different. We tailor testing to your architecture, use cases, integrations, and exposure level so the engagement reflects how your system actually operates.
Built for Security-Conscious Teams
Our engagements are designed to support technical and executive audiences alike, helping organizations understand risk clearly while giving internal teams concrete guidance for remediation and maturity.
Redbot Tests AI The Way Attackers Target It
We do not treat AI security as a novelty or a checklist exercise. Redbot evaluates how adversaries influence system behavior, extract sensitive information, abuse connected tools, and chain weaknesses into real operational impact.
Frequently Asked Questions About AI & LLM Security Testing
Get clear answers to common questions about AI security testing, what it covers, how it differs from traditional testing, and how Redbot evaluates real-world risk across AI-enabled systems.
What is AI and LLM security testing?
AI and LLM security testing identifies vulnerabilities in AI-driven systems, including prompt injection, data leakage, unsafe output generation, insecure integrations, and weak trust boundaries.
Unlike traditional application security testing, AI security testing focuses on how models interpret inputs, handle context, interact with data sources, and influence downstream actions across connected systems.
How is AI security testing different from penetration testing?
Penetration testing focuses on finding exploitable weaknesses in applications, infrastructure, and networks. AI security testing focuses on how models behave under abuse, how attackers manipulate prompts and context, and how AI integrations create new attack paths.
Redbot evaluates both the model and the surrounding ecosystem, including tools, retrieval layers, permissions, workflows, and the ways attackers chain AI-specific weaknesses into broader system impact.
What types of AI systems can be tested?
Redbot can assess customer-facing AI assistants, internal copilots, LLM-powered workflows, retrieval-augmented generation systems, agent-based tools, model-integrated applications, and AI systems connected to APIs, plugins, or internal knowledge sources.
Testing is scoped to reflect how your environment actually operates, including the specific models, tools, permissions, data flows, and business logic involved.
What does AI security testing typically include?
A typical engagement may include prompt injection testing, context manipulation, data leakage analysis, guardrail bypass validation, integration abuse testing, unsafe tool invocation review, and multi-step adversarial scenario testing.
The exact scope depends on the system architecture, exposure level, risk profile, and how the AI system interacts with internal or external services.
How long does an AI security testing engagement take?
Engagement timelines vary based on complexity, integrations, available environments, and the depth of testing required. Simpler assessments may move quickly, while larger AI deployments with multiple tools, data sources, and workflows require broader adversarial validation.
Redbot scopes each engagement around your architecture and objectives so the testing reflects real exposure instead of forcing a one-size-fits-all model.
Why does AI security testing matter before production deployment?
AI systems can introduce new attack paths before teams fully understand how they behave under adversarial conditions. Testing before or during deployment helps identify exploitable weaknesses before those risks affect users, internal operations, or sensitive data.
This is especially important when AI systems influence decisions, access business data, or trigger actions across connected applications and workflows.
AI Security Insights & Threat Research
Explore real-world AI attack techniques, emerging LLM vulnerabilities, and security research from the Redbot team. These insights reinforce how adversaries are actively targeting AI systems today.
How Prompt Injection Attacks Bypass AI Safeguards
Learn how attackers manipulate model instructions to override safeguards, extract sensitive data, and influence system behavior across integrated environments.
Read Analysis →How AI Systems Expose Sensitive Data Without Detection
Understand how context windows, retrieval pipelines, and poorly scoped integrations lead to unintended data exposure in production AI systems.
Read Analysis →Chaining AI Vulnerabilities Into Real-World Exploits
Explore how attackers combine prompt injection, tool access, and system logic flaws to escalate impact beyond individual model interactions.
Read Analysis →Stay Current On AI Threat Evolution
Redbot research helps security teams understand how emerging AI attack techniques translate into practical business risk. Use these insights to stay informed, validate assumptions, and strengthen how you assess AI exposure over time.
