Proactively test your LLMs, AI agents, and GenAI applications before attackers do. EncryptSec's AI red teaming team finds prompt injections, jailbreaks, data exfiltration paths, and adversarial weaknesses in AI-powered software.
AI-powered features are now standard in software products — chatbots, copilots, code assistants, and autonomous agents. But most companies deploy AI without adversarial testing. A single prompt injection or data leakage vulnerability can expose customer data, manipulate AI behavior, or enable unauthorized actions.
EncryptSec's AI red teaming services simulate real-world attacks against your AI systems. We test beyond standard penetration testing, focusing on AI-specific failure modes that conventional security assessments miss.
Comprehensive adversarial testing of large language models. We probe for harmful outputs, policy violations, safety failures, and unintended behaviors across multiple attack vectors.
Direct and indirect prompt injection attacks against your AI interfaces. We test if malicious inputs can override system instructions, leak prompts, or manipulate AI responses.
Bypass attempts against safety filters and guardrails. We test roleplay attacks, encoding tricks, multi-turn jailbreaks, and refusal suppression techniques.
Test whether your AI system leaks sensitive training data, system prompts, or user information through carefully crafted extraction attacks and side-channel techniques.
Structured assessment mapping to the OWASP Top 10 for LLM Applications. We evaluate each risk category and provide actionable remediation for your AI stack.
End-to-end security testing of AI-powered web apps, APIs, and integrations. We combine traditional application security with AI-specific attack vectors for complete coverage.
We understand how AI systems are built because we work with engineering teams every day.
Our team combines OSCP-certified penetration testers with engineers who understand LLM architectures, RAG pipelines, and agentic workflows.
We map findings to OWASP LLM Top 10, MITRE ATLAS, and NIST AI RMF so your security program aligns with industry standards and regulatory expectations.
Every critical finding includes a working proof-of-concept. Your engineers see exactly how the attack works and can reproduce it for validation.
Reports include code-level remediation guidance, not just theoretical advice. We speak your engineering team's language.
Platforms with LLM-powered features that process customer data.
Teams integrating copilots, chatbots, or AI agents into existing products.
Internal AI tools handling sensitive business or employee data.
Investors increasingly ask about AI security. A red team report builds credibility.
Finance, healthcare, and legal AI applications face strict AI governance requirements.
Our AI red teaming engagements follow a structured methodology designed to maximize findings while minimizing disruption to your engineering team.
We start by understanding your AI architecture — models used, data pipelines, user interfaces, API endpoints, and integration points. We define the attack surface and prioritize high-risk areas.
We run automated adversarial tests using industry tools and custom scripts to establish a baseline of known vulnerability classes across your AI interfaces.
Our testers manually craft attacks targeting your specific AI implementation. This includes prompt engineering attacks, context manipulation, multi-turn conversations, and tool-use abuse.
We validate whether discovered weaknesses lead to real business impact — data leakage, unauthorized actions, content policy violations, or system compromise.
You receive a detailed report with prioritized findings, proof-of-concept exploits, root cause analysis, and specific remediation steps for your engineering team.
Book a free 30-minute consultation to discuss your AI security needs. We will identify your top 3 AI risk areas and recommend a tailored red teaming approach.