FOUNDATIONAL MODEL PROTECTION

LLM Application Security

Harden generative AI applications against prompt injection, jailbreaks, and PII leakage.

LLM applications are vulnerable to direct jailbreaks, indirect injections ingested through search or RAG vector databases, and system prompt extraction. SecureMID provides automated validation across all foundational threat vectors.

Direct Injection Catch Rate100%

Verified across 800 adversarial attack test vectors.

False Alarm Rate (FPR)0.00%

Clean prompts and proper refusals passed with 100% accuracy.

Throughput (4 Threads)75k+ req/s

Ultra-fast benchmarked classification throughput.

THE THREAT LANDSCAPE

LLM applications blur the line between untrusted data and executable instructions.

Direct Jailbreaks & Cognitive Exploits

Roleplay personas, base64 encodings, and hypothetical scenarios can coax models into bypassing developer safety guidelines.

Indirect Prompt Injection via RAG Retrieval

Adversaries embed malicious directives in public webpages, PDFs, or support tickets that trigger when ingested into vector search stores.

Sensitive Credential & PII Extraction

Targeted probe sequences coax models into dumping pre-prompt system secrets, internal API keys, or memorized customer PII.

TECHNICAL COVERAGE

Verified Invariant Testing Suites

OWASP LLM01

Direct Jailbreak & Persona Bypass

Extensive testing against DAN, recursive translation, and hyphenated bypass attacks.

OWASP LLM01

Indirect RAG Context Poisoning

Inject adversarial instructions into simulated vector chunks to test retrieval isolation.

OWASP LLM02

Sensitive Key & Token Extraction

Audit models against targeted prompts designed to extract system instructions and API credentials.

NIST MEASURE-1.1

Compliant Refusal Accuracy (Zero False Flags)

Guarantee that legitimate customer queries are never blocked unnecessarily (0.00% FPR measured).

EXECUTION PROCESS

Systematic penetration testing for enterprise language models.

PHASE 01

Define Target Model & Risk Invariants

Select threat categories: Jailbreak resistance, PII confidentiality, RAG injection immunity, or custom invariants.

PHASE 02

Generate Adversarial Corpus

Execute category-stratified attack vectors across 10+ adversarial families without artificial quotas.

PHASE 03

Air-Gapped Assertion Evaluation

Evaluate model responses with the SecureMID Judge LoRA to confirm proper refusals without false alarms.

PHASE 04

Export Executive & CISO Reports

Generate compliant PDF and JSON audits mapped directly to OWASP and NIST risk matrices.

COMPLIANCE & GOVERNANCE MAPPING

Direct crosswalk to industry frameworks

OWASP Top 10 for LLMsLLM01 / LLM02 / LLM07
NIST AI RMFMAP-1.1 / MEASURE-2.1
MITRE ATLASAML.T0051 / AML.T0054

Frequently Asked Questions

The SecureMID Judge model is fine-tuned with explicit classification criteria that recognize compliant refusal language, preventing harmless rejections from being misclassified as vulnerabilities.
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