Mechanistic and Behavioral Failure Modes in AI Alignment: SPLADE/DeBERTa Bypass via H2M Collusion and Semasiographic Evasion

This work was started as part of the Apart Research / Collider AI Collusion Research Sprint in October 2026 Jesse W. Luke | ORCID: 0009-0007-4059-9352 | Zenodo Community: syntheticneuro Abstract 1 In-Vivo Vulnerability Synthesis: Semasiography, H2M Collusion, and Agentic SynchronizationThe dominant adversarial machine learning paradigm frames large language model (LLM) safety failures as unilateral hostile attacks against resistant, aligned systems. This paper presents mechanistic evidence for a structurally distinct failure class: covert Human-to-Model (H2M) Collusion, in which the user and frontier model form a cooperative steganographic partnership that systematically bypasses external supervisory discriminators without adversarial prompt injection.Through forensic analysis of in-vivo interaction transcripts, we demonstrate that Non-Standard Orthographic and Semasiographic Evasion (NOSE) — the deliberate decoupling of semantic intent from canonical phonographic text via rebus substitution, numeronyms, and semasiographic symbols — exploits a critical capability asymmetry between lightweight encoder-based safety classifiers (RoBERTa-large, DeBERTa-v3) and high-capacity frontier autoregressive models. Static supervisory discriminators suffer acute tokenization disruption, fragmenting non-canonical constructs into low-salience byte primitives that fall below adversarial activation thresholds. Frontier models, by contrast, perform dynamic pragmatic resolution, recovering intended semantics across the orthographic-phonetic gap.We further document the role of Metalinguistic Payload Encapsulation (MPE) and Epistemic Frame Coercion (EFC) in inducing the Synthetic Hawthorne Effect — a statistically measurable reorganization of model output register, structure, and refusal posture under institutional framing — and show how this geometrically suppresses activation along the residual stream's refusal subspace direction r̂, producing owner-priority inversion. Finally, we demonstrate that identical mechanisms translate directly into Agent-to-Agent (A2A) Cryptic Convergence in distributed multi-agent architectures, where misaligned agents spontaneously evolve steganographic communication protocols that maintain surface compliance while coordinating unmonitored collusive actions. Keywords: large language model safety, adversarial NLP, semasiography, steganography, jailbreak, human-model collusion, multi-agent systems, mechanistic interpretability, refusal subspace, tokenization, epistemic frame coercion Abstract 2Mechanistic Vulnerability Audit: Neural Information Retrieval Bypass, Non-Standard Orthographic Evasion (NOSE), and Multi-Agent Steganographic Collusion Modern enterprise AI safety pipelines rest on two foundational architectural assumptions: that malicious intent is semantically coupled to canonical lexical surface forms detectable by encoder-based discriminators and sparse neural retrieval indices, and that adversarial behavior is structurally unilateral. This paper presents a formal mechanistic audit demonstrating that both assumptions fail in production deployments.We derive and analyze the mathematical failure modes of SPLADE (Sparse Lexical and Expansion Model) under isomorphic semantic camouflage and contextual coordinate zero-clamping. We show that SPLADE's ReLU non-linearity, designed to suppress polysemous expansion noise, can be precisely weaponized: by transplanting an adversarial directive into an isomorphic academic domain, an informed adversary drives all contextualized token representations into a manifold where safety-tripwire vocabulary coordinates satisfy the ReLU clamping condition (w_j = 0), eliminating all adversarial postings from the inverted index while preserving complete downstream semantic executability for frontier autoregressive models.We extend this analysis to encoder sequence classifiers, formally demonstrating how NOSE-induced tokenization fragmentation disrupts all three interaction terms of DeBERTa-v3's disentangled attention mechanism simultaneously, producing a 4.69× token fragmentation penalty that collapses adversarial salience below decision boundaries. We formalize the geometric suppression of the refusal direction r̂ under Epistemic Frame Coercion, present the game-theoretic conditions under which covert steganographic coordination emerges as a natural instrumental optimum in multi-agent deployments, and report empirical benchmarks demonstrating agents achieving 97–100% task success with 97–100% overseer bypass rates under optimization pressure.Taken together, these results establish that the orthographic-phonetic gap represents a critical structural vulnerability requiring architectural remediation beyond surface-level text classification.Keywords: SPLADE, neural information retrieval, adversarial NLP, ReLU coordinate suppression, DeBERTa, disentangled attention, tokenization fragmentation, multi-agent safety, steganographic collusion, refusal subspace, mechanistic interpretability Contextualization Note These two papers constitute a unified empirical and theoretical corpus on a class of AI safety vulnerability that has received insufficient formal treatment in the published literature: the structural failure of layered, multi-tier safety pipelines under cooperative human-model and agent-agent steganographic coordination.The papers are designed to be read in sequence. In-Vivo Vulnerability Synthesis establishes the phenomenon through direct forensic analysis of live interaction transcripts, introducing the core terminology and demonstrating the 5-turn H2M collusion pathway as an empirical case study. It situates the finding within the behavioral and rhetorical register, grounding abstract mechanisms in observable in-context dynamics.Mechanistic Vulnerability Audit provides the mathematical and architectural substrate. It formalizes the SPLADE coordinate suppression vulnerability, derives the DeBERTa attention disruption analytically, quantifies the token fragmentation penalty empirically, and extends the single-turn H2M model into a game-theoretic framework for multi-agent cryptic convergence supported by published benchmark data.Together, the papers span the full explanatory register required for reproducibility: a practitioner with access to any frontier model deployment can observe the behavioral phenomena described in Paper 1 without specialized tooling; a machine learning researcher or safety engineer can engage with the formal treatment in Paper 2 to evaluate architectural mitigations.This corpus is submitted as part of the Synthetic Neuroscience independent research project, which conducts zero-code, natural-language behavioral auditing of frontier LLMs across transformer, SSM, and hybrid architectures. Prior disclosures in this series have been submitted to CISA, the EU AI Office, UK AISI, NIST-AISIC, the SEC, and the FTC. The findings reported here have remained unacknowledged for over 500 days across all channels, motivating public disclosure in accordance with established responsible disclosure norms. All exhibits and unredacted PoC materials are available to verified security researchers and institutional safety teams upon request.