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QIF-T0058

high

Calibration poisoning

Tier 3 — Demonstrated (Lab-proven)

Legacy status: DEMONSTRATED

Subtly corrupt BCI calibration data to maintain influence over signal interpretation across device resets. Cryptographically signed calibration data prevents. Recalibration audits detect drift.

Technique Details

Tactic
QIF-C.IM
Status
DEMONSTRATED
Bands
S1, S2

Therapeutic Application

Poisoning BCI calibration process to establish persistent attacker advantage

Clinical Analog

Adaptive BCI calibration for patients with changing neural dynamics

Treats

  • progressive neurological conditions
  • post-stroke recovery
  • pediatric BCI (growth adaptation)

Neural Impact

2 of 7 neural bands affected

S1 S2

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Scoring

NISS v1.1 NISS:1.1/BI:L/CR:H/CD:H/CV:I/RV:P/NP:T
CVSS v4.0 CVSS:4.0/AV:A/AC:L/AT:P/PR:L/UI:N/VC:L/VI:H/VA:L/SC:L/SI:H/SA:L
6.0Medium
BICRCDCVRVNP
 

Governance

Neurorights at Risk

This technique threatens 5 of the 4 proposed neurorights (Ienca & Andorno, 2017).

Consent Complexity
0.96 / 4.0

FDORA §3305 Compliance

Non-Cyber Device (missing: software)
Regulatory Coverage
0.8 / 1.0
524B Requirements
TM VA SA PM
Regulatory Gaps
  • ! CVSS partially captures risk; neural dimensions missing
  • ! Consent complexity under-matches neural impact (CCI/NISS mismatch)

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) 6.0 6.0 Medium -
Child (10yr) + ADHD 6.0 7.1 High +1.06
Adult with ALS 6.0 7.0 Medium +0.97

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