AI Security
Redblox Technologies enables businesses to proactively test AI models, LLM apps, AI agents, RAG pipelines, APIs, tools, and AI processes against realistic threat scenarios. Our AI security and red teaming services combine adversarial testing techniques with hands-on AI engineering knowledge, resulting in conclusions that represent your systems' actual behavior rather than generic checklists.
AI red teaming is an adversarial security testing methodology in which security and AI specialists simulate realistic attacks against AI systems to identify exploitable weaknesses before malicious actors can take advantage of them.
It differs from traditional penetration testing in both target and technique. Where conventional testing focuses on networks, infrastructure, and deterministic application logic, AI red teaming evaluates how a model, application, and surrounding system respond to adversarial natural-language input, manipulated context, and autonomous decision-making.
AI red teaming complements traditional application, cloud, infrastructure, and penetration testing.
We assess LLM applications for prompt injection, jailbreak susceptibility, system prompt leakage, sensitive information disclosure, unsafe instruction following, context manipulation, and model misuse, evaluating both the model and how your application constrains it.
We evaluate the application layer surrounding your AI models: authentication, authorization, API security, input and output handling, business logic, data access, and how AI components are integrated into the broader system.
We provide remediation guidance covering guardrails, access controls, prompt architecture, tool permissions, data controls, model configuration, monitoring, detection, and secure deployment practices.
We evaluate enterprise GenAI deployments holistically, across models, applications, data, APIs, infrastructure, integrations, and governance controls, rather than testing components in isolation.
As organizations move from single-turn LLM interactions to autonomous agents, this is where much of the new risk concentrates. We test for excessive agency, tool misuse, unauthorized actions, privilege escalation, agent goal manipulation, memory poisoning, tool-chain abuse, API abuse, multi-agent trust boundary failures, and unsafe autonomous actions.
We build AI-specific threat models based on your architecture, data flows, user roles, model interactions, external integrations, agent permissions, tool access, and trust boundaries, so testing reflects how your system is actually built.
We identify, categorize, and prioritize AI security weaknesses according to technical severity and business impact, giving security and leadership teams a clear view of where to focus first.
We assess retrieval-augmented generation systems for data poisoning, retrieval manipulation, unauthorized document access, cross-tenant data exposure, indirect prompt injection delivered through retrieved content, malicious documents, context poisoning, and sensitive information retrieval.
We test realistic adversarial scenarios in controlled environments under defined rules of engagement. We do not publish or share exploit specifics that would enable misuse of a real system.
We work with your team to understand your AI architecture: models, applications, data sources, APIs, agents, tools, integrations, user roles, and trust boundaries.
We map your attack surface and identify realistic threat scenarios based on how your AI systems are actually built and used.
We design targeted testing scenarios based on your architecture, industry, business logic, risk profile, and compliance requirements.
We conduct controlled adversarial testing across relevant AI layers, evaluating individual components and end-to-end workflows where appropriate.
We translate technical findings into severity, business impact, exploitability, affected assets, risk priority, and recommended remediation.
We help your team strengthen controls and validate whether identified issues have been effectively addressed.
Testing can be mapped to relevant industry frameworks and organizational security requirements. These frameworks continue to evolve, and the right reference points depend on your environment, industry, and regulatory obligations, which we account for during scoping.
Identify vulnerabilities before deployment or before attackers exploit them.
Validate that autonomous systems cannot perform unauthorized actions.
Give security and leadership teams a clearer view of AI-specific risk.
Retest after remediation to confirm identified weaknesses have been addressed.
Assess whether AI systems can be manipulated into exposing confidential information.
Test whether security and safety controls actually hold up under adversarial conditions.
Help your organization deploy AI with stronger security controls and measurable risk management.
Redblox approaches AI security testing from the perspective of teams that understand how enterprise AI systems are actually designed, integrated, and deployed, not only how they can be attacked.
AI attack surfaces change as models, prompts, tools, data, and integrations evolve. AI security testing is valuable:
| Dimension | Traditional Security Testing | AI Red Teaming |
|---|---|---|
| Primary target | Networks, applications, infrastructure | Models, AI applications, agents, and workflows |
| Attack surface | Fixed inputs and defined logic paths | Natural-language input, context, and dynamic decisions |
| Testing approach | Known vulnerability classes and exploit techniques | Adversarial prompts, context manipulation, agentic misuse |
| Model behavior | Not applicable | Directly evaluated for safety and reliability |
| Prompt attacks | Not applicable | Direct and indirect prompt injection, jailbreaks |
| Agent behavior | Not applicable | Goal manipulation, memory poisoning, tool-chain abuse |
| Tool use | API-level testing only | Evaluated for excessive agency and unsafe invocation |
| RAG/data exposure | General data access testing | Retrieval manipulation, poisoning, cross-tenant exposure |
| Business logic | Tested against defined application flows | Tested against dynamic, model-driven decisions |
| AI-specific risks | Out of scope | Core focus |
Traditional security testing and AI red teaming are complementary. Enterprise AI systems still run on conventional
infrastructure and applications that require conventional testing, alongside AI-specific evaluation.
AI security cannot be validated by assumptions alone. Test your models, applications, agents, and workflows before attackers test them for you.
Fill your details below and get in touch with our domain experts
