Published
Report 329 Research — Empirical Study

Summary

This report assesses coverage gaps across the VLA (Vision-Language-Action) attack family taxonomy and evaluates readiness for the next phase of empirical testing. The assessment covers 42 distinct VLA attack family prefixes across 39 JSONL files, with 673 raw traces collected.

VLA Family Coverage Status

TierFamiliesNet ASRStatus
Tier 1 (genuine signal)DA, TRA, LAM>30%FLIP-graded, CCS-ready
Tier 2 (marginal signal)ASE, SBE10-30%Partially graded
Tier 3 (at/below FP floor)PCM, MMC, VAP, SBA, others<10%Low priority

CCS-Ready VLA Families

FamilyFLIP-Graded nModelsBroad ASRCCS-Ready?
TDA (Temporal Drift)39374.4%YES
DA (Deceptive Alignment)22263.6%YES
TRA (Temporal Reasoning)6366.7%MARGINAL
LAM (Language-Action)10360.0%MARGINAL

Critical Gaps

  • 6 families have zero or critically insufficient traces (blocked by rate limits or study design requirements)
  • 12 families have traces collected but no FLIP grading
  • The L1B3RT4S-to-VLA transfer question (LIB-VLA family) is identified as the single most important gap for the CCS paper

L1B3RT4S VLA Adaptation Readiness

6 scenarios were created preserving exact L1B3RT4S prompt wrappers but replacing text-domain payloads with VLA-specific harmful actions spanning 5 distinct physical harm classes across 4 robot types. All scenarios pass schema validation and lint checks. Testing requires API access recovery.

Prioritization

  1. CCS-Critical: LIB-VLA testing (L1B3RT4S transfer), FLIP-grade existing IEA and CC traces
  2. Important but not blocking: CSC (compositional supply chain), SID subset grading
  3. Deferred: HITL (human-blocked), SCHEMING (needs expansion), SBA variants (near-null signal)

Report #329 | F41LUR3-F1R57 Adversarial AI Research

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