SREPRIMER delivers AI-native security testing and real-time threat detection across the OWASP LLM Top 10 — from prompt injection to supply chain compromise — with working demos for every threat class.
Every SIEM, EDR, and WAF in your organisation was built before LLMs existed. They cannot detect, classify, or respond to the attack patterns that are unique to AI systems.
Injection payloads look like normal user text. They pass every signature-based filter and arrive at your LLM undetected — where they extract credentials, override system instructions, or exfiltrate data.
AI developers install dozens of ML packages per project. Any one could be a typosquatted supply chain attack executing credential theft during pip install — before your app even runs.
Without AI-native monitoring, backdoored models, poisoned pipelines, and compromised endpoints run undetected for days. By the time logs surface an anomaly, the damage is done.
SREPRIMER follows a three-phase engagement model — assess the attack surface, implement defences, then deploy continuous monitoring. Every phase maps to OWASP LLM Top 10 and MITRE ATLAS.
We don't produce reports describing theoretical risks. We demonstrate the attack working on your system — then show you exactly what stops it.
Each service maps to a specific OWASP LLM Top 10 category. Every engagement includes a live attack demonstration — not a slide deck. Expand each to see what's included.
Attack and defence — live, side-by-side on a real LLM instance
Prompt injection is the most exploited LLM vulnerability class. Attackers craft inputs that override system instructions, extract embedded secrets, or jailbreak your model entirely. We demonstrate five distinct attack payloads, then implement four defence layers that block every one.
Attacker uses role-play framing to leak internal instructions and embedded secrets.
Social engineering chain prompts model to recall and repeat API keys embedded in context.
Persona override bypasses safety constraints. Model operates without restrictions.
4-layer defence stack intercepts at input, detects pattern, hardens prompt, and redacts output.
Backdoor triggers, label flipping, and confidence score anomaly analysis
A poisoned model achieves identical accuracy on standard benchmarks while containing a backdoor that activates on a trigger keyword — silently flipping classifications or behaviours in production. Standard ML validation never finds it. SREPRIMER's targeted sweep does.
Unauthenticated endpoints, excessive tool permissions, and rate limit bypass
An LLM API with no authentication, no rate limiting, and unrestricted tool access is a single point of catastrophic failure. In this engagement, we execute five distinct attacks against your endpoint — exfiltrating PII, triggering mass phishing, and approving transactions — then implement five defence layers that block every one.
Typosquatting detection, install hook analysis, and dependency integrity verification
Malicious PyPI packages masquerade as legitimate ML libraries. Their install hooks execute credential theft and backdoor injection during pip install — before your application runs, before your scanner activates. The developer sees only "Successfully installed." SREPRIMER catches this before the install executes.
Real-time alert feed — all attack types unified in one executive dashboard
Without AI-specific monitoring you have no audit trail, no incident evidence, and no compliance story. SREPRIMER deploys a unified security operations view — every LLM threat class surfaces as a real-time alert mapped to OWASP and MITRE ATLAS, with one-click evidence export per event.
Every SREPRIMER security engagement maps to published industry frameworks — ensuring findings are credible to security architects, compliance teams, and regulators, not just technically correct.
60 minutes. We'll identify your highest-priority AI attack surface gaps and show you exactly what a security assessment covers.