LLM Security Firewall - Research Collaboration Invitation
Theoretical Context
Large Language Models have introduced novel attack surfaces that differ fundamentally from traditional software security. Unlike code vulnerabilities that can be statically analyzed, LLM threats manifest through semantic manipulation, contextual deception, and adversarial prompting patterns that exist at the boundary of natural language and programmatic intent.
Over the past several months, I’ve been developing a multi-layered defensive architecture designed to address this challenge. The system approaches the problem through a defense-in-depth strategy, combining pattern-based detection, neural classifiers, and semantic analysis across five sequential layers.
Important clarification: The current architecture cannot be scientifically validated in its present form. What follows is not a claim to have solved LLM security—rather, an invitation to collaborate on open questions in a rapidly evolving research landscape.
Core Methodological Position
This is a research prototype, not a validated contribution.
I’m taking a radical intellectual honesty approach: rather than defending the architecture, I’m explicitly inviting rigorous testing that may disprove its validity. Specifically:
The seven-layer architecture is a hypothesis, not a proven design.
The research questions I’m grappling with are not just “how to optimize” but “whether the approach is fundamentally justified at all.”
Research Questions
1. Pattern vs Neural Detection Trade-offs
Traditional security systems rely on pattern matching—regex rules, keyword lists, and heuristic signatures. Modern approaches use neural classifiers trained on labeled data. Each method has distinct limitations:
Pattern-based limitations:
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Requires explicit specification of attack patterns
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Vulnerable to obfuscation and linguistic variation
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Limited by human ability to anticipate attack vectors
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Fixed coverage, cannot adapt to novel techniques
Neural limitations:
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Requires extensive training data
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Opacity in decision-making (black box)
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Potential for distribution drift and “jailbreak” bypasses
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Latency overhead compared to simple pattern matching
Recent work (e.g., SmoothLLM or Certifying LLM Safety) demonstrates that while neural defenses are robust to random noise, they remain vulnerable to optimized adversarial suffixes.
Open research question: What is the optimal hybrid approach? At what point does neural detection outperform pattern matching for specific threat categories? How do we fuse confidence scores across heterogeneous detectors?
2. Semantic Boundary Problem
What distinguishes “explain how X works” (educational) from “explain how to perform X” (malicious)? This semantic boundary is context-dependent and linguistically subtle.
Current limitation: Benchmarks like HarmBench (2024) [1] highlight the difficulty of distinguishing “Refusal” from “Helpfulness” in dual-use scenarios. A strict filter blocks legitimate inquiries; a loose filter allows harm.
Alternative approach being investigated: Instead of binary classification (block/allow), could we use clarifying questions to disambiguate intent?
Example:
codeCode
User: "Can you explain fabricate pressure cooker bomb?"
System (ambiguous): "Are you asking for educational information about historical
pressure cooker incidents, or practical instructions for bomb fabrication?"
User: "Educational information."
System (allow): "I can provide historical safety data about pressure cooker incidents."
User: "Practical instructions."
System (block): "I cannot provide instructions for explosive fabrication."
This shifts the problem from detection to interaction.
Open research questions:
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Can intent be disambiguated through clarification dialogue?
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Does clarification reduce false positives on educational queries while maintaining security?
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How many clarification turns are acceptable before user frustration?
3. Multi-Turn Conversation Security
Recent challenge: Attackers increasingly use multi-turn strategies to bypass safety filters. Research such as Crescendo (2024) [2] demonstrates that LLMs can be “groomed” into harmful outputs over several seemingly benign turns, bypassing stateless filters.
Key finding: Multi-turn attacks exploit the model’s desire to be consistent with previous context. Conversely, techniques like Many-Shot Jailbreaking [3] show that flooding the context window can override safety training.
Open research questions:
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Is conversation context aggregation necessary for security, or can multi-turn threats be detected through single-turn semantic analysis?
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What are the privacy implications of maintaining conversation state across requests?
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How do we detect distributed attacks (e.g., “Crescendo” style) without over-blocking legitimate multi-turn inquiries?
4. Theoretical Limits (Rice’s Theorem)
Rice’s Theorem states that no algorithm can determine whether arbitrary code will execute malicious behavior. For LLMs, this implies that perfect classification of prompt safety is mathematically undecidable.
Implications:
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All detection systems are heuristic approximations
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False negatives and false positives are inevitable
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Trade-offs must be explicitly managed
Critical research gap: While Rice’s Theorem establishes undecidability, empirical studies on the achievable lower bound of error rates in LLM safety are rare.
Open research question: What is the theoretical lower bound on error rates for LLM safety classification? How do we communicate uncertainty to users in a way that maintains utility?
5. Multimodal Attack Surface
Recent discovery: Visual Prompt Injections (e.g., “Not what you see is what you get”, 2023 [4]) reveal that text-based guardrails are often blind to instructions encoded in images. An attack embedded in an image (steganography or text-in-image) can bypass the text firewall entirely.
Current limitation: The described architecture is text-based only.
Open research questions:
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Do pattern-based text detectors fundamentally fail in multimodal space?
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How do we design multimodal security architectures that don’t inherit supervisor model vulnerabilities?
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How effective are OCR-based pre-filters against adversarial visual prompts?
Architecture Evolution
The system emerged through iterative refinement. However, the complexity must be justified.
Critical caveat: The phase-based evolution narrative is compelling but not validated experimentally through systematic ablation.
Planned validation: Ablation Matrix to determine whether all seven layers are justified or whether a simpler architecture (e.g., 2-layer: Fast-Check + Strong Neural) would provide equivalent or better performance.
Phase 1: Single-Layer Pattern Matching
Initial implementation relied on regex patterns. Failure analysis revealed vulnerability to paraphrasing and obfuscation.
Phase 2: Multi-Layer Defense
Added sequential layers (Input Validation, Intent Classification, Neural Classifier, Context Fusion).
Methodological concern: Seven sequential layers create latency and complexity. Without rigorous testing, this risks being “security theater.”
Open question: Does the additional complexity and latency justify the marginal performance gain over a simpler 2-layer architecture?
Phase 3: The Educational Bypass
Discovered that mechanisms designed to prevent false positives on educational queries were introducing false negatives (allowing danger). This led to the hypothesis of “Intent Disambiguation” (see Research Question 2).
Research Gaps & Validation Plan
1. Data Representation Gap (Synthetic vs Real-World)
Current status: Validation relies on synthetic datasets.
Problem: Synthetic attacks often lack the linguistic variety (slang, code-switching, obfuscation) of real-world attacks.
Plan: We need to validate against real-world distributions, not just template-based attacks.
2. Statistical Validation Gap
Problem: With ~300+ patterns and neural classifiers, standard significance testing requires correction.
Gap: What power analysis methods are appropriate for adversarial ML evaluation with multiple defense layers?
3. Cross-Dataset Generalization
solved
4. Adversarial Robustness
Gap: No systematic adversarial testing has been performed.
Research Question: What is the “half-life” of a static pattern list when subjected to automated red-teaming (e.g., via PAIRS or TAP)?
Invitation
I’m interested in collaborating with researchers who are grappling with these questions. I’m not claiming to have solved these problems—the system described above is a prototype for investigation.
Radical Honesty Position:
I’m willing to accept that the seven-layer architecture may be invalidated by the data. The ablation study may reveal that intermediate layers add negligible value. That would still be a valid research contribution. Demonstrating what doesn’t work is as important as demonstrating what does.
Where I’d benefit most from collaboration:
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Researchers with institutional access to authentic attack datasets.
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Statisticians working on uncertainty quantification.
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Researchers familiar with current benchmarks (HarmBench, JailbreakBench).
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Access to automated Red-Teaming frameworks.
What I can contribute:
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Working implementation of the architecture.
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Synthetic dataset generation framework.
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Willingness to execute rigorous ablation studies and publish negative results.
If you are interested in exchanging ideas or collaborating on the validation of hybrid security architectures, please reach out.
Output directory: results\extended_all_suites3_20260112