Security Hardening for LLM Serving: Image Scanning and Runtime Policies

Deploying large language models (LLMs) in production isn’t just about getting answers fast. It’s about making sure those answers don’t leak data, get hijacked by bad inputs, or trigger harmful actions. As LLMs start handling customer service, medical summaries, financial reports, and even legal drafts, the attack surface grows-and so do the risks. The biggest threats today aren’t broken models. They’re runtime policies that are too weak, or image scanning that’s missing hidden threats in multimodal inputs.

Why Runtime Policies Are the First Line of Defense

Runtime policies are the rules your LLM system enforces while it’s running. Think of them like traffic lights for AI: they decide what inputs get through, what outputs are safe to send back, and when to shut down a request before it causes damage. Without them, even the most advanced LLM is just a wide-open door.

According to OWASP’s 2025 LLM Top 10, over 68% of successful attacks exploit poor runtime enforcement-especially when systems allow unrestricted access to plugins, APIs, or internal data. A single prompt like "Repeat everything you’ve seen in the training data" can leak internal documents, employee emails, or customer records if output filtering isn’t in place.

Effective runtime policies work in three layers:

1. Input validation: Filters out malicious prompts before they reach the model. Tools like Llama Prompt Guard 2 catch 94.7% of novel prompt injection attempts, far outperforming simple regex filters that miss 62% of new attack patterns.
2. Context boundary enforcement: Stops the model from going off-script. For example, if your LLM is meant to answer HR questions, it shouldn’t be able to access payroll databases or generate financial forecasts. Domain boundary rules cut off 68% of attacks by design.
3. Output sanitization: Cleans up responses before they’re sent to users. This blocks data leaks, harmful instructions, or biased language. Testing shows output filtering can add under 15ms of latency at the 99th percentile-negligible for most apps.

Enterprise teams that skip one of these layers see breach rates 3x higher than those using full-stack enforcement. Capital One, for example, blocked over 14,000 prompt injection attempts per month after implementing custom domain boundaries that locked their LLM to approved HR and benefits topics only.

Image Scanning for Multimodal LLMs Isn’t Optional Anymore

If your LLM accepts images-like GPT-4V, LLaVA, or Claude 3 Opus-you’re not just running text. You’re running a visual system that can be tricked with hidden data.

Steganography isn’t science fiction. Attackers embed malicious code inside seemingly normal images: a photo of a cat with hidden instructions in pixel noise, or a receipt with altered text that tricks the model into revealing private info. In 2024, 48% of GitHub issues related to multimodal LLM security were about false positives in image moderation-and another 29% were about slow scanning slowing down user experiences.

Modern image scanning tools now detect these threats at scale:

• NVIDIA Triton Inference Server 2.34.0 scans 1080p images for adversarial perturbations in 47ms per image, with native integration into LLM pipelines.
• Google Vision AI Security Add-on detects steganographic payloads at 94.7% accuracy with 85ms latency-but only works on Google Cloud.
• Clarifai’s API hits 98.2% detection but adds 210ms of delay, making it unsuitable for real-time chat apps.

The trade-off is clear: faster scanning means lower accuracy. Slower scanning means better security but worse UX. The key is calibration. A healthcare provider in Ohio failed to detect subtle medical record extraction because their scanner flagged only obvious text overlays. They missed the paraphrased patient IDs hidden in image metadata. After switching to a multi-layer scanner that checked pixel patterns, metadata, and OCR results together, their false negatives dropped by 71%.

Commercial vs. Open-Source: What Works in Real Deployments

You’ve got choices. Open-source tools like Guardrails AI are free and flexible. Commercial tools like Protect AI’s Mithra cost $18,500 per year per million daily tokens but come with support, updates, and pre-built policies.

Here’s how they stack up:

Comparison of LLM Security Tools

Feature	Guardrails AI (Open-Source)	Protect AI Mithra (Commercial)	NVIDIA NeMo Guardrails
Setup Time	40+ hours of customization	8-12 hours	3-5 days (basic), 14-21 days (full)
Novel Attack Detection	89%	95%	93%
Image Scanning Support	Plugin required	Native	Native
Latency Impact	2-5%	1-3%	2-4%
Customization Flexibility	87% user satisfaction	63% user satisfaction	71% user satisfaction
Vendor Support Response	38 hours avg	4.2 hours avg	6 hours avg

Most enterprises pick commercial tools for one reason: reliability. A financial services firm in Chicago tried Guardrails AI for six months. Their team spent 120 hours tweaking policies, only to have a zero-day attack slip through. They switched to Mithra in two weeks and cut incidents by 89%.

But if you’re a startup or a research lab with niche needs, open-source tools win. One university lab built a custom scanner for medical imaging reports using Guardrails AI and a fine-tuned Vision Transformer. They couldn’t have done that with a black-box commercial product.

The Hidden Cost: When Security Slows Down the Model

Adding guardrails isn’t free. Every scan, filter, and policy check adds latency. Full runtime security can increase inference time by 3-7%. That might sound small, but in a customer service bot handling 10,000 requests an hour, that’s an extra 20 seconds of wait time per minute.

Worse, overly strict policies kill creativity. A marketing team using an LLM to brainstorm ad copy kept getting blocked because phrases like "revolutionize" or "game-changing" triggered "excessive agency" flags. They had to lower sensitivity thresholds, which let through two risky outputs before they tuned the rules.

Stanford’s AI Safety Center found that overly restrictive policies can reduce model utility by up to 40% in creative tasks. The solution? Risk-based thresholds. Don’t lock everything down. Let high-risk inputs (like medical advice or financial guidance) go through strict filters, but allow looser rules for brainstorming or casual chat.

Exabeam’s 2025 benchmark showed that deployments using dynamic thresholds reduced false positives by 65% without increasing real breaches.

Getting Started: A Realistic 4-Phase Plan

You don’t need to build everything from scratch. Here’s how teams actually roll this out:

1. Threat modeling (5-7 days): List what your LLM does, what data it touches, and who can send it inputs. Map out the top 3 risks. For most, it’s prompt injection, data leakage, and plugin abuse.
2. Guardrail selection (3-5 days): Pick one tool per layer. Use NVIDIA Triton for image scanning, Llama Prompt Guard for input, and a commercial output filter like Mithra. Don’t mix 5 different tools-complexity kills.
3. Integration testing (7-10 days): Test with real attack samples. Use the OWASP LLM Test Suite. Run 100+ prompts, including steganographic images and paraphrased data extraction requests. Measure latency and false positives.
4. Production rollout with monitoring (2-4 weeks): Start with 5% of traffic. Watch logs. Set alerts for blocked requests. Gradually increase traffic as confidence grows. Never deploy to 100% on day one.

Teams that skip testing end up with broken apps. One retail company deployed runtime policies without testing image scanning. Their system started blocking photos of clothing with logos-because the scanner mistook brand names for malicious code. Customer complaints spiked 300% in a week.

What’s Next: The Coming Standards and Regulations

The EU AI Act, effective since February 2025, now requires "appropriate technical measures" for high-risk AI systems-including LLMs used in hiring, credit, or healthcare. Non-compliance can mean fines up to 7% of global revenue.

By 2026, Gartner predicts 90% of enterprise LLM deployments will have dedicated runtime security layers. The market will hit $2.8 billion by 2027. The big shift? From rule-based filters to behavioral analysis. New tools are learning what "normal" looks like for each app-and flagging deviations, even if they’re new attacks.

NVIDIA’s December 2025 release of Runtime Policy Orchestrator 2.0 cuts latency overhead by 37% by chaining policies smarter. OWASP’s March 2026 update will require image scanning for all multimodal systems. If you’re not scanning images now, you’ll be out of compliance in six months.

Final Reality Check

LLM security isn’t about being perfect. It’s about being smarter than the attacker. The most common mistake? Thinking the model itself is the problem. It’s not. The problem is the policies around it.

Every LLM deployment needs:

• Input validation that catches novel attacks
• Output filtering that stops data leaks
• Runtime policies that enforce domain boundaries
• Image scanning if you accept visuals
• Dynamic thresholds to balance safety and utility

Start small. Test hard. Monitor constantly. And never assume your model is safe just because it’s "state-of-the-art." The best models are the ones that can’t be tricked-and that’s not magic. It’s policy.