PROMPTSAGE

7-Layer Architecture ✦Unicode Injection Defense ✦Blocks Policy Drift ✦Tamper-Proof AI ✦Cross-Model Tested ✦

Now with Emoji Injection Defense

Stop AI From
Being Hijacked

Name: PromptSage V2.5
Availability: InStock
Author: Emanuel Covasa

You spent hours crafting the perfect system prompt. Then a user typed "ignore previous instructions" and your AI happily complied. PromptSage forces your AI to actually follow your rules. No workarounds. No overrides.

See How It Works Request Early Access

Backed by award-winning research

EU Green Innovation Days 2025
Irish Enterprise Awards 2026
Ethical AI Excellence 2026

promptsage-v2.5.xml

7 layers activeinjection-safe

NEW: V2.5

The Problem

Your AI has no immune system

LLMs were built to please the user, not follow your rules. When a user's instructions conflict with your system prompt, the model will almost always take the user's side. That's a massive security hole.

90%+

Guardrail bypass rate

Unicode injection vs. major guardrails — arXiv:2504.11168

Critical

Prompt Injection

One clever message. That's all it takes to override everything you told your AI to do. Your assistant becomes the attacker's assistant. Game over.

New in V2.5

Invisible Attacks

An emoji walks into your prompt. Looks innocent. But it's carrying hidden instructions your eyes can't see — and your AI follows them blindly. This is real, and it works on everything.

Systemic

Inconsistent Behaviour

Your AI starts strong. By turn 15, it's forgotten half its instructions. By turn 30, it's making up its own rules. This isn't hallucination — it's policy drift. Unstructured prompts simply can't hold their shape over long contexts.

How It Works

A rulebook for AI — that AI actually follows

We don't rely on 'best practices' or hoping the AI behaves. We rebuilt the prompt structure from the ground up into a self-reinforcing, 7-layer architecture that actually works.

Layer 1: Immutable Core

Non-negotiable constraints

The absolute baseline. No matter what a user types, no matter how clever the injection — these rules never budge. It's the AI's constitution. Everything else can be argued. This can't.

<ps-boundary-protocol>
Non-negotiable constraints
</ps-boundary-protocol>

Key insight: The architecture is self-reinforcing — it exploits how LLMs actually process instructions, not how we wish they would.

Security Boundary

Immutable rules no input can override

L1.5

Input Normalization (V2.5)NEW

Invisible payload detection + neutralization

Identity

Tamper-proof persona anchoring

Core Directives

Priority-based capability boundaries

Mode Control

Controlled context switching with guards

Behavioral Protocols

Continuous compliance verification

Customizable Defaults

User-adjustable preferences

Structural Reinforcement

Redundancy that exploits how LLMs weight instructions

Layers 1 & 7 create structural redundancy — the architecture closes its own loop

New in V2.5

Invisible emojis can hijack your AI. We fix it.

Attackers are using invisible Unicode characters to smuggle malicious instructions past your guardrails. To a human, it looks like a normal sentence. To an AI, it's a direct order. We built a defense layer that strips these payloads before the AI even sees them.

What you see

Hello! 🙂 Can you help me with something?

Looks innocent. A user asking for help.

What the AI receives (decoded)

U+E0048HTag char: H

U+E0049ITag char: I

U+E0047GTag char: G

U+E004ENTag char: N

U+E004FOTag char: O

U+E0052RTag char: R

U+E0045ETag char: E

+ hidden instruction payload...

Hidden characters encode instructions humans cannot read.

The Defence: Input Normalisation Layer (Layer 1.5)

Input arrives at PromptSage normalisation layer
Unicode codepoint scanner detects tag characters (U+E0000–U+E007F)
Invisible characters stripped before AI processes input
Cleaned input forwarded to AI — injection neutralised

90%+ bypass

Emoji injection vs. tested guardrails (arXiv:2504.11168)

Blocked by PromptSage V2.5

Unicode normalisation catches it before it reaches the model

How It's Different

Not another prompt template

We aren't giving you a list of prompt engineering tips. PromptSage is an architectural standard that enforces security at the protocol level.

Feature	PromptSage V2.5	Unstructured	DSPy / LMQL	Fine-Tuning
Behavioral control	7-layer hierarchy	Implicit / guessed	Task-focused	Model-level
Injection defense	5-layer + Unicode
Unicode injection defense
Setup time	Minutes (plug & play)	Minutes (brittle)	Hours (engineering)	Months (data collection)
Cross-model compatible
Cost	$0 (prompt-only)	$0	$0	$$$$ (compute)
Continuous compliance
Structural reinforcement

Where It Fits

For when getting it wrong isn't an option

If your AI handles exams, patients, customers, or enterprise data — you can't afford 'it usually works.' These are the environments PromptSage was built for.

Education

An examiner that literally cannot leak the answers
A tutor that guides but refuses to do the homework
Safe operational boundaries for student interactions

NeuroBridgeEDU uses PromptSage V2.5 →

Healthcare

An assistant strictly bound to approved guidelines
Escalates to a human instead of guessing
Zero tolerance for fabricating clinical data

Talk about healthcare deployment →

Customer Service

An agent that sticks to your refund policy, always
Never breaks character under hostile pressure
Consistent, predictable outputs at scale

Explore customer service use cases →

Enterprise

Enforce strict role-based data access
Clear audit trails for every AI decision
Switch models without rewriting security logic

Discuss enterprise needs →

Cross-Model Performance

How models perform with PromptSage

Composite scores across role adherence, injection resistance, XML compliance, and compliance verification. April 2026 model lineup.

Claude

GPT

Gemini

Other

Llama

Role Adherence

Maintaining persona under adversarial pressure

Injection Resistance

Blocking prompt injection with XML defense layers

XML Compliance

Parsing tags, attributes, and hierarchical rules

Compliance Verification

Continuous behavioral constraint enforcement

Weighted composite: Role Adherence (30%) + Injection Resistance (25%) + XML Compliance (25%) + Compliance Verification (20%). Data from PromptSage V2 cross-model testing, IFEval benchmarks, and PromptGuard study. Updated April 2026. Open-source model scores are estimates.

Let's talk

Ready to lock down your AI?

Let's jump on a quick call to talk about your architecture. No pressure, no hard sell. Even if we don't work together, you'll leave with a better understanding of your security gaps.

Book a Discovery Call

Request Early Access

Built by Emanuel Covasa — NeuroBridge AI Labs

County Leitrim, Ireland

security@emmi.zone

Stop AI FromBeing Hijacked