failure-modes-invisible-and-compounding

IN derived (depth 3)

Created 2026-06-21T11:31:42+00:00 · Reviewed 2026-06-21T15:37:01+00:00

Neural networks exhibit complementary failure modes — hallucinations and adversarial vulnerability — that stem from the statistical nature of connectionist computation, while the attack surface expands at every ML pipeline boundary (model inference, training data, synthetic data feedback loops), creating a compound vulnerability surface where each stage's output becomes the next stage's potential weakness.

Justifications

SL — Complementary internal failure modes (hallucination + adversarial) combine with expanding boundary attacks (poisoning + collapse) to create a failure surface invisible to any single defense layer

Antecedents (all must be IN):

IN hallucination-and-adversarial-are-complementary-neural-failures — Neural networks exhibit two failure modes that rule-based and symbolic approaches largely avoid — hallucinations (generating false content presented as fact) and adversarial vulnerability (susceptibility to deliberately perturbed inputs across architectures and domains). Rule-based ML, by contrast, produces interpretable rules rather than opaque statistical mappings. These contrasting properties suggest that hallucinations and adversarial vulnerability may be intrinsic tendencies of the connectionist paradigm rather than purely engineering deficiencies, though this does not preclude mitigation strategies or imply symbolic systems are free of their own failure modes.
IN attack-surface-expands-at-every-ml-boundary — ML systems face adversarial threats across multiple stages — adversarial examples exploit model inference (perturbations cause misclassification across supervised and reinforcement learning), data poisoning targets the training pipeline (injected false data degrades model performance), and model collapse can degrade models trained on uncurated synthetic data — suggesting a broad attack surface where vulnerabilities span different phases of the ML lifecycle.

Dependents

These beliefs depend on this one:

IN failure-modes-compound-unobserved-and-unmitigated — Neural network failure modes compound across pipeline stages while simultaneously invisible to standard evaluation AND unmitigated by any functioning safety mechanism at any level — adversarial, poisoning, and collapse attacks chain across inference, training, and generation boundaries, while the safety net (theoretical foundations, practical defenses, deployment safeguards) is comprehensively absent.