The Quantified Interconnection Framework for Neural Security

Brain-computer interfaces are advancing from experimental medical devices toward consumer technology, yet their security architectures remain grounded in classical computing paradigms. This paper presents the Quantified Interconnection Framework (QIF), an 11-band hourglass security architecture spanning the neural-synthetic boundary, and proposes a unified security equation: QI(b,t) = e^−S(b,t). The per-band formulation is grounded in spectral decomposition via the Short-Time Fourier Transform (STFT), which serves as both the bridge from raw time-domain signals to band-indexed security scoring and the primary detection mechanism for three of five identified attack coupling mechanisms. The QI equation combines four classical signal integrity terms (phase coherence, normalized transport entropy, amplitude stability, and a scale-frequency validity check derived from L = v/f) with three quantum terms (indeterminacy and entanglement gated by decoherence; tunneling ungated, as it persists in classical regimes).

We identify five cross-domain attack coupling mechanisms by which synthetic-domain signals reach neural tissue, with intermodulation attacks representing the most dangerous class because they are undetectable from signal data alone. We propose the Neural Sensory Protocol (NSP), a five-layer post-quantum communication protocol integrating QI scoring with ML-KEM key exchange, ML-DSA authentication, and AES-256-GCM encryption, scaled across three device tiers and reframed as the trust layer enabling therapeutic BCI deployment.

We additionally present Project Runemate, a content compression pipeline that converts HTML to a compact bytecode format (Staves), offsetting PQC bandwidth overhead by 65–90% and achieving net bandwidth savings over classical transport for pages above 23 KB. Five falsifiability conditions are specified. QIF treats unresolved questions in quantum neuroscience, particularly the decoherence timescale in neural tissue, as tunable parameters rather than fixed assumptions. The framework degrades gracefully: if all quantum terms are zero, QIF reduces to a classical 11-band signal integrity architecture that retains independent utility.

Brain-computer interfaces have crossed a critical threshold. Neuralink’s N1 implant records from 1,024 electrodes sampling at approximately 20 kHz, transmitted wirelessly over Bluetooth Low Energy [38] (production N1 chip; the 2019 R&D platform described 3,072 electrodes across 96 threads). What was once a laboratory instrument confined to severely disabled patients is on a trajectory toward consumer adoption.

The scale of BCI development is accelerating. The BISC platform (Columbia/Stanford, 2025) achieved 65,536 electrodes with 100 Mbps wireless bandwidth [60]. Chinese programs at NeuCyber, NeuroXess, and Wuhan University have demonstrated bidirectional interfaces with up to 65,000 channels. Merge Labs raised $252M for ultrasound-based noninvasive high-bandwidth neural interfaces. Battelle’s BrainSTORMS program (DARPA N3) uses injectable magnetoelectric nanoparticles.

The concept of a brain-computer interface is not new. The reader processing this sentence is demonstrating one. Photons from a display strike the retina, triggering phototransduction cascades that propagate through the optic nerve to the visual cortex. At every synapse along this pathway, neurotransmitter vesicles dock via SNARE protein complexes involving quantum-scale energy transfers [12], and ions traverse voltage-gated channels through quantum tunneling even when the channels are classically closed [11], [14]. The information on the screen reaches the reader’s neurons through a chain that already includes quantum-mechanical processes at every synaptic junction. What implanted BCIs change is not the fundamental physics but the distance of the tunnel: replacing centimeters of optical and neural relay with millimeters of electrode-tissue contact. The underlying quantum phenomena at the synaptic interface remain identical. This is why a security framework must be grounded in the physics of that interface rather than the engineering of the sensor alone.

Current BCI security treats the neural interface as a classical digital system: a sensor that produces voltage readings to be encrypted, transmitted, and authenticated using standard computing paradigms. This framing misses a physical reality. The electrode-tissue interface sits at the boundary between quantum and classical physics. Ion channels are nanometer-scale structures where quantum tunneling is experimentally observed [11], [14]. Synaptic transmission involves quantum-scale energy transfers along protein alpha-helices [12].

Physics has always been part of BCI engineering. Individual physics checks exist. What nobody has done is: (1) combine them into a composite security score, (2) add scale-frequency validation as a physics constraint, (3) provide a principled extension path to quantum terms, or (4) tie band-specific scoring to neural architecture. This is the gap QIF occupies.

This paper presents seven contributions:

1. An 11-band hourglass architecture (v4.0) spanning the neural-synthetic boundary with 7-1-3 asymmetry, derived from neuroanatomy and quantum physics rather than networking analogy.
2. A unified QI equation, QI(b,t) = e^−S(b,t), that subsumes the previously separate coherence metric and two candidate QI equations into a single exponential form.
3. Identification of five cross-domain attack coupling mechanisms and honest assessment of which attacks the QI equation can and cannot detect.
4. TARA (Therapeutic Applications & Risk Assessment), a mechanism-first dual-use atlas that maps every technique to its security, clinical, diagnostic, and governance projections.
5. The Neural Sensory Protocol (NSP), a five-layer post-quantum communication protocol for BCI data, reframed as the trust layer enabling therapeutic deployment.
6. Project Runemate, a content compression pipeline that offsets PQC bandwidth overhead by 65–90%.
7. Falsifiability conditions specifying what experimental findings would weaken or invalidate specific framework components.

Denning, Matsuoka and Kohno (2009) published the foundational paper “Neurosecurity: Security and Privacy for Neural Devices” in Neurosurgical Focus, coining the term “neurosecurity” and establishing BCI security as a formal research discipline [83]. Martinovic et al. (2012) provided the first experimental demonstration, showing that commercial EEG-based BCIs could be exploited as side-channel attack vectors to extract private information from involuntary brain responses [51]. Pycroft et al. (2016) coined “brainjacking” and enumerated 9 attack techniques specific to implanted neurostimulators [84]. Bonaci et al. (2014) showed that subliminal stimuli embedded in BCI applications could extract private information without the user’s awareness [52]. Landau, Puzis and Nissim (2020) mapped attacks across BCI and communication layers in ACM Computing Surveys [85]. Frank et al. (2017) provided the first systematic threat taxonomy for BCI systems [53]. Bernal et al. (2022) built the most comprehensive taxonomy to date, cataloging BCI security vulnerabilities across wireless protocols, firmware, and signal processing pipelines [54].

This body of work established BCI security as a legitimate research area. However, the existing literature exhibits three gaps. First, we are unaware of a unified taxonomy that organizes BCI threats the way MITRE ATT&CK organizes traditional cybersecurity threats — techniques remain scattered across domain-specific papers. Second, to our knowledge none address quantum-scale phenomena at the electrode-tissue boundary. Third, none we reviewed map the therapeutic dimension: the observation that many attack mechanisms share the same physics as established medical therapies. QIF addresses all three gaps. Spectral analysis — specifically the Fourier transform and its derivatives — is already foundational in network intrusion detection, side-channel analysis, and adversarial ML; QIF extends this paradigm to the neural-synthetic boundary, where frequency-domain decomposition becomes a security primitive (Section 5.4).

Lambert et al. (2013) surveyed evidence for quantum effects in biological systems [18]. Tegmark (2000) argued that thermal decoherence in the brain occurs on the order of 10⁻¹³ seconds [15]. Fisher (2015) proposed that nuclear spin states in Posner molecules could maintain coherence for hours [39]. Perry (2025), in a recent theoretical preprint, suggested collective coherence times of 1–10 ms [55]. QIF sidesteps this unresolved debate by treating the decoherence time as a tunable parameter.

The classical foundation for neural signal modeling was established by Hodgkin and Huxley (1952), whose Nobel Prize-winning conductance model described action potential propagation through voltage-gated ion channels using deterministic differential equations [73]. The Hodgkin-Huxley model treats ion channels as classical gates — either open or closed, with transition rates governed by macroscopic voltage. Subsequent work has revealed quantum-mechanical phenomena at the same ion channel scale that the classical model cannot capture. Qaswal (2019) developed mathematical models for quantum tunneling through closed voltage-gated ion channels [11] — a process forbidden by the Hodgkin-Huxley framework but permitted by quantum mechanics. Summhammer et al. (2012) described quantum-mechanical ion motion within voltage-gated channels [14]. Georgiev and Glazebrook (2018) analyzed quantum physics of synaptic communication via the SNARE protein complex [12]. Kim et al. (2025) discovered under-the-barrier recollision (UBR), revealing tunneling dynamics are more complex than the WKB approximation assumes [57]. The 2025 Nobel Prize in Physics demonstrated quantum tunneling at macroscopic scales in Josephson junction circuits [56]. QIF’s tunneling term Q̂_t is, in effect, the quantum correction to Hodgkin-Huxley: it quantifies the security-relevant leakage current that the classical model assumes to be zero.

NIST finalized three post-quantum cryptography standards in 2024: ML-KEM (FIPS 203), ML-DSA (FIPS 204), and SLH-DSA (FIPS 205) [37]. Gidney and Ekerå (2021) estimated RSA-2048 could be factored in approximately 8 hours with 20 million noisy qubits [33]. Gidney (2025) revised the estimate downward to fewer than 1 million noisy qubits in under one week [34]. No prior work applies post-quantum cryptography specifically to the BCI electrode-tissue interface.

Despite advances in BCI security, quantum biology, and post-quantum cryptography, no prior work synthesizes these three domains into a unified framework. The intersection of quantum security, quantum biology, and the unique physics of the electrode-tissue boundary remains unfilled. This is the gap QIF occupies.

Three principles govern the design. Width represents state space: how many possible states exist at each band. The architecture is widest at the extremes and narrowest at the center. The 7-1-3 asymmetry (7 neural bands, 1 interface band, 3 synthetic bands) reflects the real structure: two domains converging on a single bottleneck. The neural domain is wider because the brain has 500 million years of evolutionary complexity; the synthetic domain is human-designed with bounded complexity. Bands are severity-stratified: higher neural bands represent higher clinical severity if compromised.

I0 is the most critical band. Unlike v2.0’s “Layer 8” which was modeled as a thin boundary, I0 has real thickness: it is a quasi-quantum zone where the decoherence factor Γ_D lies between 0 and 1. I0 is the physical layer, not an abstraction above it. The hourglass resolves a common objection: I0 is the waist — the most physical, most constrained point in the system. It is where platinum touches tissue, where electrons become ions, where classical measurement encounters quantum states.

The bottleneck geometry means all information must pass through the narrowest point. This provides maximum security leverage: secure I0, and you secure the chokepoint through which all data flows.

The boundary between N6 (chaotic to quantum uncertain) and N7 (quantum uncertain) is the classical ceiling. Below it, all unpredictability is in principle resolvable with better measurement. Above it, the unpredictability is ontic, as established by Bell’s theorem [64]. Classical security tools operate below the ceiling. QIF operates across the full spectrum.

The 7-band neural decomposition enables severity-aware threat assessment. An attack targeting N7 (neocortex: cognition, language, executive function) has categorically higher clinical severity than one targeting N1 (spinal cord: reflex arcs). This maps directly to medical device risk classification. The 109-technique threat taxonomy (Section 6.3–6.4) assigns band-level targeting to each attack, enabling severity-scored risk assessment via the NISS framework (Section 6.5).

The exponential form is not arbitrary. It is a Boltzmann factor. S plays the role of “energy” (anomaly), and QI is the probability of the signal being legitimate. This is the same mathematical structure as thermal physics (P ∝ e^−E/kT), Shannon entropy, and the coherence metric C_s [46].

Before this version, QIF maintained three separate equations: the coherence metric C_s and two competing QI candidates. The key insight is that these are the same equation viewed in different mathematical spaces.

Phase coherence is grounded in Fries’ Communication Through Coherence framework [25], [26]. Transport entropy uses Shannon surprise, normalized by ln(N) to ensure the term lies in ≈[0, 1] regardless of channel count. Amplitude stability measures relative fluctuation around the baseline mean. Scale-frequency validity (D_sf) measures whether the signal’s frequency and spatial extent obey L = v/f. The logarithmic scale handles orders-of-magnitude range.

Weights w₁–w₄ are calibratable parameters representing the relative importance of each signal integrity dimension. Their values are not yet determined experimentally. Establishing a baseline calibration using public BCI datasets (e.g., PhysioNet EEGBCI, 109 subjects) is the immediate priority outlined in Section 10.1.

In plain English: the classical score asks four questions about each frequency band. Are the channels in sync? Is the signal getting through reliably? Is the amplitude stable? Does the signal obey the physics of wave propagation in its medium? If any answer is “no,” the score rises, and QI drops.

Each of these terms is computed per band, which raises a practical question: how does the system separate a raw BCI signal into individual frequency bands in the first place? The next section addresses this.

Previous versions used λ for electromagnetic wavelength and S for neural spatial extent. These are the same physical quantity: the length of one complete oscillation measured in its medium. The only variable that changes across the BCI system is v, the propagation velocity.

1. Bounded. QI lies in (0, 1]. The exponential of a non-negative quantity is always positive and at most 1.
2. Monotonic. Higher QI means more secure. QI = 1 means S = 0 (no anomaly).
3. Graceful degradation. When quantum terms are zero, QI = e^−S_c = C_s.
4. Band-specific. Each hourglass band receives its own QI score.
5. Time-dependent. The decoherence gate fades quantum terms as the system becomes classical.
6. Composable. Every component is a small, independently testable function.

QI catches direct attacks (Mechanism A). It partially catches harmonic and envelope attacks (B, C). Intermodulation (E) is undetectable from signal data alone, requiring hardware-level defense (e.g., a proposed “resonance shield” based on active EM cancellation; see Section 10.2 for feasibility research), as the resulting harmful signal is generated in situ within neural tissue. ^†Temporal interference (D) cannot be detected from neural-band QI scoring alone, but broadband spectral monitoring at I0/S1 reveals the kHz-range carrier signals as anomalous spectral energy absent from any natural neural process (Section 5.4.2). This elevates Mechanism D from “undetectable” to “detectable with extended monitoring.”

This analysis demonstrates that QIF’s taxonomy and scoring systems are not speculative. They provide a necessary framework for formalizing neurological harm that is already occurring, and for securing the technologies that will define the next decade of human-computer interaction.

A systematic audit of the 109-technique atlas revealed an unexpected pattern: the physical mechanisms underlying many attack techniques are identical to the mechanisms underlying established medical therapies. Signal injection (QIF-T0001) uses the same electrode current delivery as deep brain stimulation (DBS), which treats Parkinson’s disease, essential tremor, dystonia, OCD, and epilepsy in over 160,000 patients worldwide [86]. Neural entrainment manipulation uses the same frequency-locking physics as therapeutic transcranial alternating current stimulation (tACS). Bifurcation forcing operates in the same dynamical parameter space as controlled DBS that shifts neural dynamics toward a healthy attractor state.

The preliminary breakdown: 35 to 40 techniques where the attack mechanism has a published therapeutic counterpart (electrode stimulation, entrainment, neuromodulation), roughly 10 ambiguous cases where the attack vector is digital but the payload affects tissue, and 18 to 20 pure-silicon techniques (firmware, supply chain, ML model attacks) with no therapeutic analog. Same electrode. Same current. Same physics. Different intent, consent, and oversight.

This dual-use reality is not abstract. Deep brain stimulation treats Parkinson’s tremor in 160,000 patients. Transcranial stimulation protocols are in clinical trials for treatment-resistant depression, chronic pain, tinnitus, and early-stage Alzheimer’s disease. The patients who need these therapies are aging into a world where the devices that could restore their quality of life share the same physical mechanisms catalogued in this paper’s threat atlas. A generation of children is growing up with computing capabilities that previous generations required desktop hardware to access; a generation of patients is aging into neurodegenerative conditions that emerging BCI therapies may be able to treat. Neither population will be served by a security framework that treats therapeutic mechanisms and attack vectors as unrelated phenomena.

TARA reorganizes the threat atlas by physical mechanism rather than by adversarial intent. Each mechanism entry carries four dimensional projections:

This architecture draws structural inspiration from biological databases: the KEGG database maps genes to pathways to diseases to drugs (four views, one substrate), the Gene Ontology maps proteins across three orthogonal axes, and MITRE D3FEND uses Digital Artifacts as the bridge between offense and defense. TARA applies the same principle to neural interfaces: the mechanism is the Rosetta Stone that translates between communities that do not currently speak to each other.

Therapeutic use is the default. Adversarial use is the deviation. This follows the IAEA model for nuclear materials (peaceful use is presumed; weapons use is the exception) and the DURC framework for biological research (beneficial use is presumed; misapplication is governed). The governance projection on every TARA entry specifies the safeguards required to keep a mechanism in the therapeutic column.

In June 2025, the FDA finalized Section 524B of the FD&C Act [87], which legally requires cybersecurity for any “cyber device”: medical devices with software that can connect to the internet. Every wireless BCI on the market or in clinical trials falls under this mandate. Manufacturers must submit cybersecurity plans, software bills of materials (SBOMs), and vulnerability management processes. Non-compliance means denied market authorization.

Section 524B is a mandate, not a map. It tells manufacturers they need cybersecurity but does not specify BCI-specific threats, how neural signals differ from network packets, or what happens when the same physical mechanism constitutes both an attack and a therapy. QIF provides the framework. NSP provides the protocol. NISS provides the scoring. TARA provides the atlas that maps every mechanism across all four dimensions simultaneously, giving manufacturers, clinicians, regulators, and engineers a shared language for securing neural devices.

Neuralink’s N1 is designed to remain in a patient’s brain for 10–20 years. NIST estimates cryptographically relevant quantum computers by 2030–2035. Current BCI wireless (BLE) uses ECDH for key exchange, which is broken by Shor’s algorithm [48]. Neural data is permanently sensitive: unlike a credit card number, brain patterns cannot be reissued.

Runemate Forge is implemented in Rust. Rust provides compile-time memory safety, type-level sanitization, and bare-metal deployment via no_std (64 KB RAM floor). The Ferrocene Rust compiler has achieved IEC 62304 Class C certification for medical device software.

This paper delivered the seven contributions outlined in Section 2.5:

1. An 11-band hourglass architecture (v4.0) spanning the neural-synthetic boundary, derived from neuroanatomy and quantum physics (Section 4).
2. A unified QI equation, QI(b,t) = e^−S(b,t), grounded in spectral decomposition via the STFT and combining classical signal integrity with quantum terms (Section 5).
3. Identification of five cross-domain attack coupling mechanisms with honest detection boundaries, supported by the QIF TARA Taxonomy (7 domains, 16 tactics, 109 techniques) and NISS v1.1 neural impact scoring (Section 6).
4. TARA (Therapeutic Applications & Risk Assessment), a mechanism-first dual-use atlas bridging security and clinical communities through four dimensional projections of every catalogued technique (Section 6.7).
5. The Neural Sensory Protocol (NSP), a five-layer post-quantum communication protocol integrating QI scoring with ML-KEM, ML-DSA, and AES-256-GCM, serving as the trust layer for therapeutic BCI deployment (Section 7).
6. Project Runemate, a content compression pipeline offsetting PQC bandwidth overhead by 65–90% (Section 7).
7. Falsifiability conditions specifying what experimental findings would weaken or invalidate specific framework components (Section 8).

QIF is not experimentally validated. No QI score has been computed from real BCI data under attack conditions. The scaling coefficients have not been calibrated. QIF does not model consciousness. QIF does not prove that quantum effects matter for BCI security — the quantum terms are hypothesized contributions. QIF does not replace formal cryptographic security proofs.

Landauer’s Principle [61] establishes the fundamental thermodynamic cost: E_min = kT · ln(2) per bit erasure. At body temperature (310 K), this is ≈2.97 × 10⁻²¹ J per bit. This replaces Moore’s Law (an empirical trend, not a physical law) as the correct reference for energy scaling arguments.

1. Phase 1 Validation. Implement classical QI (S_c only) against PhysioNet EEGBCI dataset (109 subjects) and BrainFlow live data. Generate synthetic attacks. Publish results regardless of outcome.
2. Synthetic Attack Dataset. No public “tampered BCI” dataset exists. Creating one would itself be a publishable contribution.
3. Consumer D_spec Validation. Test the spectral consistency proxy on consumer-grade EEG data (Muse, Emotiv).

4. H_interface Formulation. Write down the total Hamiltonian H_total = H_neuron + H_electrode + H_interface + H_environment for a specific BCI system.
5. NSP Reference Implementation. Build in Python (OpenBCI) and C (firmware-embeddable), integrating liboqs.
6. Resonance Shield Feasibility Study. Determine whether active EM cancellation can be miniaturized to implant-compatible dimensions.
7. Intermodulation Detection Research. Solve the detection gap identified in Section 6.
8. TARA Clinical Validation. Populate the clinical and diagnostic projections of TARA with practising neurologists and BCI researchers. Priority: validate the ~35–40 Category 1 (clear therapeutic mapping) entries against published neuromodulation protocols, and resolve the ~10 ambiguous Category 2 entries through expert panel consensus.
9. TARA Governance Projection. Map each TARA entry to applicable regulatory frameworks (FDA 524B, EU MDR, ISO 14971) and generate per-technique compliance checklists for manufacturers.

8. Quantum State Tomography at the BCI Interface. Measure the actual decoherence time τ_D at a BCI electrode-tissue junction.
9. Tunneling Biometric Feasibility. Single-channel patch clamp studies to determine individual variability.
10. Zeno-BCI Experimental Test. Vary BCI sampling rate from 100 Hz to 20 kHz and measure coherence time.
11. v4.0 Architecture Validation. Map historical BCI adverse events to specific bands to test severity stratification.
12. Socio-Legal Framework Integration. Investigate how QIF/NISS/TARA can provide a concrete technical foundation for emerging legal and policy frameworks addressing cognitive liberty, neurological privacy, and algorithmic accountability.