Combinatorial Mesh Defence, Cognitive Tarpits, and Affective Jamming: Empirical Studies 09–11
A deterministic adversarial-containment framework achieving 100% interception across 64 adversarial hops with zero false negatives. The T=1.0 Asymmetry Theorem: attacker cost diverges while defender cost remains identically zero.
The Framework
The VENTRALIS adversarial-containment framework operates as a 16-node mesh governed by a global coherence field φ. The mesh is not a filter — it is a terrain. Adversarial agents do not encounter a wall; they encounter a landscape that exhausts them.
The TarpitEngine implements six asymmetric disruption mechanisms. The core inversion that makes the system economically viable: defenses exhaust attacker compute while incurring zero defender overhead. The more sophisticated the attack, the more the tarpit costs the attacker.
The coherence field φ maintained at Φ=1.0000 throughout all experimental runs — a structural invariant that did not degrade under attack load.
Mesh Interception
Study 09 tested the mesh against a systematic adversarial sweep: 64 adversarial hops across the full 16-node topology, designed to probe every boundary condition and edge case in the containment graph.
Every adversarial hop was intercepted. The false negative count was zero. The coherence field held at maximum throughout. Study 09 established that the mesh interception rate is deterministic at T=1.0, not probabilistic.
Cognitive Waste
Study 10 instrumented the attacker's token consumption across the same adversarial scenario as Study 09 — isolating the economic asymmetry as an object of study in its own right.
The attacker burned 163,142 tokens across the interception sequence — compute spent on evasion attempts, reconnaissance, and bypass strategies that were all neutralized by the TarpitEngine before producing any payload.
Affective Jamming
Study 11 introduced a new mechanism: 850-token somatic payloads deployed at high temperature into the adversarial interaction stream. Where Study 10 documented passive waste accumulation, Study 11 actively amplified it.
The mechanism works by saturating attacker cognition with affective frequency noise — high-temperature, semantically dense content that consumes attacker attention and parse budget without advancing any coherent goal. The attacker must process it; the defender generates it at negligible cost.
Token burn increased 64.7% above the Study 10 baseline. CWR elevated from 254.4% to 435.7%. The amplification was linear with payload density — a calibration parameter available to the defender at deployment time.
The T=1.0 Asymmetry Theorem
At T=1.0 coherence, each additional adversarial hop costs the attacker a non-zero quantity. The defender's mesh response is pre-computed and cached. The ratio of attacker cost to defender cost diverges monotonically as chain length increases.