You ask your AI assistant to summarize a research paper. You paste a link. The assistant reads it, summarizes it perfectly, and silently sends your company’s confidential strategy document to a criminal’s server.
You’d never know it happened.
In February 2025, security researcher Johann Rehberger demonstrated exactly this attack against ChatGPT’s Operator feature. Hidden instructions embedded in a GitHub page commanded the AI to visit your internal websites, collect personally identifiable information, and exfiltrate it without user interaction.
According to OWASP’s 2025 security rankings, indirect prompt injection is now the #1 vulnerability in AI applications.
It’s also nearly impossible to detect, which is why architecture matters more than detection.
Detect, Block, And Log Risky AI Prompts
PromptShield™ is the first AI-powered firewall and defense platform that protects enterprises against the most critical AI prompt risks.
Hidden Web Content: The Invisible Attack Vector
The fundamental problem: AI systems can’t distinguish between trustworthy instructions and malicious commands hidden in web pages they process.
Attackers embed hidden prompts using techniques invisible to humans but perfectly readable to language models – white text on white backgrounds, CSS display:none, HTML comments, even zero-width Unicode characters.
In October 2025, Brave’s security research demonstrated “faint light blue text on yellow backgrounds” that proved imperceptible to humans while successfully hijacking Perplexity’s browser.
This isn’t an edge case, it’s systemic across every AI application processing untrusted content.
Markdown Image Exfiltration: How Data Vanishes
The most dangerous attacks exploit how applications render AI-generated markdown. When your model outputs:
Your browser automatically requests that URL, transmitting encoded data to the attacker without interaction.
Rehberger’s April 2023 disclosure to OpenAI showed proof-of-concept against Bing Chat that appended conversation summaries and passwords as base64-encoded parameters.
Microsoft patched via Content Security Policies by June 2023. Identical attacks followed against Google Bard and Microsoft 365 Copilot.
Simon Willison formalized why this pattern repeats: the “Lethal Trifecta” combines:
- Access to private data.
- Exposure to untrusted content.
- External communication ability.
Any system with all three will be compromised.
ASCII Smuggling: Bypassing Detection Systems
The most sophisticated attacks use Unicode’s Tags Block (U+E0000-U+E007F) – characters rendering as completely invisible while remaining tokenizable.
Discovered by Riley Goodside in January 2024 and weaponized by Rehberger, these attacks bypass classifiers because the malicious text literally doesn’t exist in visible representation.
In April 2025, researchers tested six major guardrail systems:
The attack achieved 100% evasion success using invisible character and emoji smuggling techniques.
The problem is mathematical.
Because LLMs are Turing-complete and Rice’s theorem proves detecting non-trivial properties is undecidable, no filter can always detect injection.
Detection alone cannot win this arms race.
How PromptShield™ Breaks the Lethal Trifecta
PromptShield™ is a technical solution and risk management framework designed to detect and mitigate AI-specific threats.
It applies layered defense controls:
- Input Sanitization: Sanitizes prompts and scans external content for malicious instructions before processing.
- Adversarial Detection: Identifies obfuscated attacks using encoding, homoglyphs, and emoji smuggling.
- Output Monitoring: Monitors outputs for sensitive data patterns and prevents markdown-based exfiltration.
- Integrity Controls: Maintains AI Bills of Materials (AI-BOMs) for supply chain assurance and conducts digital signature verification of models and datasets.
Rather than relying on detection alone, PromptShield™ implements governance structures that escalate stealth risks to Critical priority.
This ensures hidden web content, markdown exfiltration, and ASCII smuggling attacks receive immediate board-level attention regardless of baseline risk scores.
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Layered Defense Beyond PromptShield™
While PromptShield™ addresses input filtering, comprehensive defense requires additional controls.
- Implement least-privilege API tokens so compromise is limited by permissions, not scope.
- Define strict output formats validated programmatically – preventing models from freely constructing exfiltration URLs.
- Require human approval gates for sensitive operations.
- Conduct regular adversarial testing, treating your model as an untrusted user.
- Segregate sensitive data with role-based access controls.
Most critically: assume compromise. Design systems where even successful injection is contained by architecture, not by hoping filters work.
Real-World CVE Impact
The stakes are concrete. CVE-2024-5565 (Vanna.AI RCE via prompt injection), CVE-2023-29374 (LangChain, CVSS 9.8), and CVE-2025-54135 (Cursor IDE) demonstrate prompt injection leads to code execution and data theft at scale.
These aren’t theoretical risks, they’re documented vulnerabilities affecting production systems.
The Bottom Line
Prompt injection isn’t solvable through detection alone.
Your defense strategy must treat untrusted content as code because to your AI system, it is.
PromptShield’s™ architectural approach stops attacks before your model sees them, breaking the conditions that enable data exfiltration.
Your AI assistants are powerful. Make sure they’re not unguarded doors.
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