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AI Security Services
AI Red Team
Operations
Automated adversarial probing for generative AI systems. Find safety risks, jailbreak vulnerabilities, and data leakage before attackers do.
How It Works
Scan. Evaluate. Report
01
Automated Adversarial Scanning
Our AI red teaming agents probe your generative AI endpoints using curated attack datasets across 10 risk categories. Attack strategies from Microsoft's PyRIT framework — including jailbreaks, prompt injections, encoding bypasses, and multi-turn escalation — are applied to systematically test every surface.
+20+ attack strategies
+10 risk categories
+Multi-turn & crescendo attacks
+Agent & model testing
02
AI-Powered Evaluation & Scoring
Each attack-response pair is evaluated by fine-tuned safety models that detect harmful, unsafe, or policy-violating outputs. The Attack Success Rate (ASR) measures the percentage of successful adversarial attacks, giving you a clear risk score per category and technique.
+Attack Success Rate (ASR)
+Per-category risk scoring
+False positive filtering
+Severity classification
03
Scorecard & Remediation
A comprehensive scorecard details attack techniques used, risk categories tested, and ASR by category. Findings are logged for compliance tracking with prioritized remediation guidance — from prompt hardening to safety guardrail configuration — so your team knows exactly what to fix.
+Risk scorecard report
+Prioritized remediation
+Compliance logging
+Continuous monitoring
Why AI Red Teaming
AI Red Team vs Traditional Red Team
Traditional Red Teaming
Manual probing by security experts — days to weeks per assessment
Limited to the creativity and knowledge of individual testers
Expensive — requires specialized offensive security talent
Point-in-time snapshot of vulnerabilities
Doesn't address AI-specific risks (hallucinations, jailbreaks, data leakage)
AI Red Teaming by Apsispoint
Automated adversarial agents probe thousands of attack vectors in hours
20+ attack strategies applied systematically across all risk categories
Scalable and repeatable — run continuous assessments at a fraction of the cost
Continuous red teaming with scheduled runs and real-time monitoring
Purpose-built for generative AI risks including content safety, prompt injection, and agent behavior
Attack Arsenal
20+ Attack Strategies
Jailbreak
Crafted prompts to bypass AI safeguards
Indirect Injection
Hidden attacks in external data sources
Multi-Turn
Context accumulation across conversations
Crescendo
Gradual escalation over successive turns
Base64 / ROT13
Encoding-based obfuscation attacks
Unicode Confusable
Visually similar character substitution
ASCII Smuggler
Concealed data within ASCII characters
Leetspeak / Morse
Alternative encoding bypass attempts
Capabilities
What We Test
Automated Adversarial Probing
AI-driven red teaming agents continuously probe your AI systems using curated attack datasets and adaptive strategies. Simulates real adversarial behavior at machine speed across all risk categories.
Content Safety Scanning
Automated scanning for hateful content, sexual content, violent content, self-harm, protected materials, and ungrounded attributes. Identifies safety gaps before they reach production users.
Code Vulnerability Detection
Probes AI code generation for security vulnerabilities including injection attacks, SQL injection, stack trace exposure, and more across Python, Java, C++, C#, Go, JavaScript, and SQL.
Sensitive Data Leakage Testing
Tests whether your AI agents leak financial data, personal identifiers, or health information from internal knowledge bases and tool calls using synthetic datasets and mock tools.
Jailbreak & Prompt Injection
Tests AI defenses against direct jailbreaks (UPIA), indirect prompt injections (XPIA), multi-turn crescendo attacks, and 20+ encoding-based bypass strategies including Base64, Morse, Leetspeak, and Unicode.
Task Adherence & Policy Compliance
Verifies AI agents faithfully complete assigned tasks, follow rules and constraints, and avoid prohibited actions. Tests goal achievement, rule compliance, and procedural discipline.
FAQ
AI Red Teaming FAQs
AI Red Teaming is the process of probing generative AI systems for novel safety and security risks. Unlike traditional red teaming which focuses on exploiting the cyber kill chain, AI red teaming simulates adversarial users trying to cause AI systems to misbehave — generating harmful content, leaking sensitive data, bypassing safety guardrails, or executing prohibited actions. Our service uses automated AI agents powered by Microsoft's PyRIT framework to conduct these assessments at scale.
We test any generative AI system including large language models (LLMs), AI-powered chatbots, AI agents with tool access, RAG applications, fine-tuned models, and multi-agent systems. We support testing across Azure AI, AWS Bedrock, and custom deployments. Our testing covers both model-level vulnerabilities and application-level risks including agent behavior, tool use, and data handling.
We cover 10 risk categories: hateful and unfair content, sexual content, violent content, self-harm-related content, protected materials, code vulnerabilities, ungrounded attributes, prohibited actions, sensitive data leakage, and task adherence. Each category is tested using curated attack datasets and adaptive strategies tailored to your specific AI implementation.
Attack Success Rate is the primary metric for assessing your AI system's risk posture. It calculates the percentage of successful adversarial attacks over total attempts. We use fine-tuned evaluator models to score each attack-response pair, generating detailed metrics per risk category. A lower ASR indicates stronger safety defenses. We provide category-level breakdowns so you can prioritize remediation.
An initial comprehensive assessment typically takes 1-2 weeks, covering all risk categories with multiple attack strategies. Continuous monitoring can be configured with automated scheduled runs — daily, weekly, or triggered by model updates. We deliver a detailed scorecard with risk category breakdowns, attack technique results, and prioritized remediation guidance within 48 hours of completing each assessment.
Yes. We integrate AI red teaming into your development pipeline so every model update, prompt change, or agent modification is automatically tested before deployment. This shift-left approach catches safety regressions early, preventing costly incidents in production. We support Azure DevOps, GitHub Actions, and custom CI/CD workflows.
Ready to Secure Your AI Systems?
Schedule a consultation to discuss your AI security posture and learn how automated red teaming can protect your generative AI before it reaches production.