Defending AI, One Word at a Time
LLDF helps security + AI teams prevent, detect, and respond to language-layer and agentic AI attacks, measured with a maturity scorecard.
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"AI models can be deceived by carefully crafted prompts designed to manipulate their instructions. Our framework provides operational mapping of techniques with plain language definitions and defenses."Six Layer Defense
L1: Input Validation
Filter and sanitize all incoming prompts to neutralize potential threats before processing.
L2: Context Protection
Secure system instructions and context to prevent manipulation of core behavioral guidelines.
L3: Behavioral Boundaries
Define and enforce strict behavior limits to ensure the AI remains within safe operational parameters.
L4: Output Filtering
Validate and sanitize AI responses before delivery to prevent leakage of sensitive information.
L5: Monitoring & Detection
Real-time threat identification to detect and respond to attacks as they happen.
L6: Response & Recovery
Comprehensive incident handling and system adaptation to recover from and prevent future attacks.
From Research Notes to a Repeatable Evaluation
LLDF isn't just a framework, it's backed by a concrete research blueprint. We've documented the core thesis, threat model primitives, scoring methodology, and a full evaluation with specific data collected and experiments run.
View Full ResearchEvaluation
A phased approach: baseline your AI risk posture, implement controls, stress-test with regressions, and operationalize with dashboards and IR drills.
Experiments Run
Prompt injection, RAG poisoning, tool escalation, detection validation, and drift/change management, each with clear metrics.
"Why Now" for Enterprises
AI systems now have authority to act. The orchestration layer is the fastest-growing attack surface, and traditional AppSec can't measure it.
LLDF
Language Layer Defense Framework—Operational mapping of techniques with plain language definitions and defenses.
AI models can be deceived by carefully crafted prompts designed to manipulate their instructions, distort their reasoning, or slip harmful content past safety mechanisms. These attacks don’t involve conventional hacking; instead, they exploit how language models process text, context, memory, and examples.
The techniques below outline common strategies for bypassing safeguards, extracting sensitive data, or provoking unsafe behavior. Recognizing these patterns enables developers, security teams, and newcomers to identify risky prompts and strengthen the resilience and safety of AI systems.
Built for the teams that defend AI systems.
Whether you lead security strategy, build AI products, or operate the SOC — LLDF gives your team a shared language and measurable framework.
CISO
Strategic risk oversight and maturity scoring for AI-powered systems.
AppSec
Secure the language layer alongside traditional application security controls.
SOC
Real-time detection and response workflows for prompt-based threats.
AI Product
Build safer AI products with defense-by-design and measurable coverage.
Measurable outcomes from teams already using LLDF.
Organizations piloting the framework are seeing real improvements across their AI security posture.
Reduced Prompt Abuse
Early adopters report measurably fewer successful prompt injection and jailbreak attempts after implementing LLDF layered defenses.
Improved Detection Coverage
Standardized technique mapping closes visibility gaps across Prevent, Detect, and Respond — giving SOC teams full-spectrum coverage.
Standardized P/D/R Playbooks
Teams align on a shared Prevent / Detect / Respond taxonomy — eliminating ad-hoc approaches and accelerating incident handling.