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QIF-T0108

high

Neuromorphic mimicry attack (synaptic weight tampering)

Tier 3 — Demonstrated (Lab-proven)

Legacy status: DEMONSTRATED

Tamper with synaptic weights or inject poisoned sensory input into neuromorphic/SNN hardware (next-gen BCI processors). Evades traditional IDS by mimicking legitimate neural activity patterns. Input poisoning ~90% success, weight tampering ~83%. Traditional IDS detects only 12-15%.

Technique Details

Tactic
QIF-B.IN
Status
DEMONSTRATED
Bands
S1, S2

Therapeutic Application

Tampering with synaptic weight parameters in neuromorphic computing hardware to alter neural network inference without modifying the training data or model architecture

Neural Impact

2 of 7 neural bands affected

S1 S2

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Scoring

NISS v1.1 NISS:1.1/BI:H/CR:H/CD:H/CV:P/RV:P/NP:N
4.7Medium
PINSPINS triggers when Biological Impact is High/Critical or Reversibility is Irreversible. Indicates potential lasting harm to neural safety.
BICRCDCVRVNP
 

Governance

Neurorights at Risk

This technique threatens 2 of the 4 proposed neurorights (Ienca & Andorno, 2017).

Consent Complexity
0.24 / 4.0

FDORA §3305 Compliance

Non-Cyber Device (missing: software)
Regulatory Coverage
0.3 / 1.0
524B Requirements
TM VA PM
Regulatory Gaps
  • ! CVSS cannot express neural-specific impacts
  • ! No FDA pathway for consumer sensor exploitation

Population Vulnerability

CRB vulnerability adjustment (γ=0.30) accounts for age, diagnosis severity, consent capacity, and device dependency.

Population NISS Base Adjusted Severity Delta
Adult (Default) 4.7 4.7 Medium -
Child (10yr) + ADHD 4.7 5.5 Medium +0.83
Adult with ALS 4.7 5.5 Medium +0.76

Validation Status

Theoretical / Not yet validated. This technique has not been independently tested. See the validation dashboard for what has been tested.

Qinnovate Neural Security Atlas Edit this on GitHub