Your organisation has deployed AI — but has anyone tested it the way an attacker would? At BlockSecBrain, we deliver specialised security assessments for LLMs, GenAI applications, agentic systems, and AI-integrated infrastructure. We apply the same adversarial mindset that drives our VAPT practice — now purpose-built for the probabilistic, semantic attack surface of modern AI.
Six specialised assessment tracks covering every layer of your AI ecosystem — from model behaviour to deployment infrastructure.
We adversarially test your large language models using multi-turn escalation, jailbreaking, and injection techniques. We measure attack success rates across categories, not just binary pass/fail, and provide guardrail hardening recommendations.
AI agents with tool access, file systems, APIs, and autonomous decision-making create devastating blast radii when compromised. We test agent workflows for indirect injection, privilege escalation, tool misuse, and trust boundary failures.
Applications built on GPT, Claude, Gemini, or open-source LLMs inherit both model vulnerabilities and app-layer risks. We test RAG pipelines, vector databases, API integrations, and output handling for injection, leakage, and code execution paths.
Teams across your enterprise are quietly deploying LLMs outside IT oversight. We identify shadow AI deployments, unmonitored data flows, and unsanctioned model endpoints — before they become compliance gaps or persistent leakage channels.
Third-party LLM providers, open-weight models, fine-tuning datasets, and ML dependencies all extend your attack surface. We assess model provenance, training data integrity, plugin ecosystems, and vendor security posture.
Across all our existing VAPT services — web, mobile, cloud, IoT, automotive — we now layer in AI-enhanced analysis. AI accelerates recon, surfaces complex business logic flaws, identifies anomalous patterns, and generates targeted exploit chains faster than traditional methods alone.
Our AI security assessments are aligned to the OWASP LLM Top 10 — the industry standard for LLM vulnerability testing, updated in 2025 to reflect real production incidents.
#1 for 2 consecutive years. Appears in 73% of production AI deployments. Direct and indirect attack vectors. We test both.
Jumped from 6th to 2nd in 2025. PII leakage, system prompt exposure, API key extraction through model outputs.
Climbed to 3rd place. Compromised model weights, malicious fine-tuning datasets, vulnerable dependencies.
Corrupted training data to introduce backdoors, bias model behaviour, or degrade performance in targeted ways.
Unsanitised LLM output passed to exec/eval, SQL builders, or HTML renderers — leading to RCE, XSS, command injection.
New in 2025. AI agents with overly broad permissions taking autonomous actions — file writes, API calls, financial transactions.
New in 2025. Extraction of proprietary system prompts, business logic, and configuration via adversarial queries.
New in 2025. RAG data store poisoning, cross-user data contamination, insecure vector database access controls.
New in 2025. Hallucination exploitation, AI-generated fraud, disinformation injection into business workflows.
New in 2025. Resource exhaustion attacks — token flooding, API abuse, denial-of-wallet, and inference cost attacks.
A structured, repeatable process aligned to enterprise AI deployment realities and emerging adversarial research.
Identify all LLMs, agents, integrations, and shadow AI deployments across your environment.
Map attacker paths, trust boundaries, data flows, and tool access specific to your AI architecture.
Manual red teaming plus automated probing — measuring attack success rates, not just pass/fail.
Every finding mapped to OWASP LLM Top 10, blast radius assessed, remediation layer specified.
Free re-test after fixes are applied. We confirm attack success rate drops below acceptable thresholds.
The 2026 threat landscape has fundamentally shifted. Here's what the industry data is telling us.
Autonomous AI agents with tool access represent a new class of threat. When an agent is compromised via prompt injection, attackers can trigger file writes, API calls, data exfiltration — and transactions — without human awareness. Adversarial testing of agent workflows is no longer optional.
By 2026, shadow LLMs deployed outside IT oversight represent a significant invisible attack surface. Teams deploy private or third-party models against corporate data without approval. Sensitive information is already circulating through unapproved AI systems at most enterprises.
Cybercriminals are now using AI to automate reconnaissance, scale phishing campaigns, and carry out attacks with minimal expertise. Prompt injection playbooks are being sold on the dark web. AI has levelled the playing field between skilled attackers and opportunistic threat actors.
AI governance mandates are accelerating globally. GDPR enforcement around AI-driven data processing, emerging EU AI Act obligations, and sector-specific requirements are pushing organisations to demonstrate AI security posture — or face significant penalties.
AI models, supply chains, APIs, and business relationships all now double as attack vectors. Ransomware is evolving beyond encryption — it exploits trust itself. Agentic AI will handle portions of the ransomware attack chain autonomously, including recon and vulnerability scanning.
96% of security professionals agree AI meaningfully improves speed and efficiency of security work. Organisations investing in AI-aware security programmes — continuous red teaming, anomaly detection, and guardrail enforcement — are positioned to outpace attackers in 2026.
Ready to Red Team Your AI?
Every AI system deployed without adversarial testing is carrying invisible risk. Let's find what your AI will do under attack — before someone else does.