LLM Security & Red Teaming
We adversarially test large language models using multi-turn escalation, jailbreaking, and injection techniques, measuring attack success rates rather than binary pass and fail outcomes.
New Service - 2026
AI Security Assessment & LLM Red Teaming
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.
$10.5T
Global cybercrime cost forecast 2025
Source: Cybersecurity Ventures
73%
Production AI deployments vulnerable to prompt injection
Source: OWASP LLM Top 10, 2025
77%
Organisations already running GenAI in their security stack
Source: State of AI Cybersecurity 2026
46%
Defenders not prepared for AI-powered threats
Source: State of AI Cybersecurity 2026
Our AI Security Services
What We Test & Secure
Six specialised assessment tracks covering every layer of your AI ecosystem, from model behaviour to deployment infrastructure and governance exposure.
Agentic AI Security Assessment
We test agent workflows for indirect injection, privilege escalation, tool misuse, and trust boundary failures that can trigger devastating blast radii.
GenAI Application Security
Applications built on GPT, Claude, Gemini, or open-source models inherit both model vulnerabilities and app-layer risks. We test RAG flows, vector stores, APIs, and output handling.
Shadow AI Discovery & Governance
We identify shadow AI deployments, unsanctioned model endpoints, and unmonitored data flows before they become compliance gaps or persistent leakage channels.
AI Supply Chain Security
Third-party LLM providers, open-weight models, fine-tuning datasets, and ML dependencies all extend your attack surface. We assess provenance, integrity, and vendor risk.
AI-Assisted VAPT
Across web, mobile, cloud, IoT, automotive, and infrastructure engagements, we now layer in AI-enhanced analysis to accelerate recon and uncover complex flaw chains faster.
Assessment Framework
OWASP Top 10 for LLM Applications
Our AI security assessments are aligned to the OWASP LLM Top 10, the industry standard for LLM vulnerability testing and GenAI application security evaluation.
LLM01
Prompt Injection
Still the most dangerous class of AI attack, spanning both direct and indirect prompt manipulation paths.
LLM02
Sensitive Information Disclosure
PII leakage, system prompt exposure, and credential extraction through model outputs remain critical concerns.
LLM03
Supply Chain Vulnerabilities
Compromised model weights, malicious datasets, and vulnerable dependencies now sit directly in the AI trust chain.
LLM04
Data & Model Poisoning
Training or retrieval data can be manipulated to degrade performance, alter behaviour, or introduce backdoors.
LLM05
Improper Output Handling
Unsanitised model output passed into code execution, HTML rendering, or command paths can trigger downstream compromise.
LLM06
Excessive Agency
Overly broad permissions allow AI agents to take autonomous actions that create security and financial risk.
LLM07
System Prompt Leakage
Adversarial queries can extract proprietary prompts, business rules, and internal operational details.
LLM08
Vector & Embedding Weaknesses
Cross-user contamination, insecure vector stores, and poisoned retrieval pipelines can expose data and distort output.
LLM09
Misinformation
Hallucination exploitation and AI-driven disinformation can create fraud, workflow disruption, and trust failures.
LLM10
Unbounded Consumption
Token flooding, denial-of-wallet, and excessive inference cost attacks can quickly turn into operational issues.
How We Work
AI Security Assessment Process
A structured, repeatable process aligned to enterprise AI deployment realities and adversarial research workflows.
🗺️
AI Asset Mapping
Identify LLMs, agents, integrations, and shadow AI deployments across your environment.
🎯
Threat Modelling
Map attacker paths, trust boundaries, data flows, and tool access specific to your AI architecture.
⚔️
Adversarial Testing
Run manual red teaming and automated probing to measure actual attack success rates.
📋
Risk-Rated Report
Map every finding to the OWASP LLM Top 10 and deliver remediation layers with context.
✅
Guardrail Verification
Retest after fixes to confirm attack success rates drop below acceptable thresholds.
2026 Threat Landscape
Why AI Security Can't Wait
The threat landscape has fundamentally shifted. These themes explain why AI security needs real testing now, not after deployment.
Agentic AI - New Attack Class
Autonomous AI agents with tool access create a new threat class where prompt compromise can trigger file writes, API calls, and transactions without human awareness.
Shadow AI - Invisible Risk
Teams deploy private or third-party models against corporate data without approval, creating significant hidden attack surface.
AI-Powered Attacker Toolkits
Threat actors now use AI to automate reconnaissance, scale phishing, and lower the expertise barrier for sophisticated attacks.
Regulatory Pressure
AI governance obligations are accelerating globally, pushing organisations to demonstrate AI security posture and control maturity.
Every Dependency Is Attack Surface
Models, plugins, APIs, datasets, and business workflows all extend the trust chain and the blast radius of compromise.
Defenders Can Regain Advantage
Security teams that invest in AI-aware red teaming, anomaly detection, and guardrail verification can outpace opportunistic attackers.
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.