PISD-Eval–Frontier AI Systems

1. System Definition & Evaluation Gap

1.1 System Class

This framework concerns frontier large language models (LLMs) deployed via API or product interfaces and subject to iterative post-training updates. These systems are characterized by:

• Large-scale pretraining followed by alignment fine-tuning (e.g., RLHF or related methods)
• Policy-conditioned refusal or constraint behaviors
• Layered safety mechanisms, including output filtering, safety classifiers, and rule-based constraints
• Periodic model version releases and policy updates
• High-volume, heterogeneous real-world user interaction

The deployment environment includes both benign and adversarial users interacting across diverse domains, with continuous exposure to novel prompt distributions.

1.2 Intervention Types

The framework focuses on behavioral changes following post-training safety interventions, including:

• Safety fine-tuning updates (e.g., RLHF or supervised alignment adjustments)
• Policy revisions affecting refusal thresholds or disallowed content definitions
• Modifications to output filtering or safety classifier models
• Deployment of new mitigation layers (e.g., content filters, monitoring systems)
• Full model version releases incorporating updated training mixtures or alignment objectives

These interventions alter model behavior in intended domains but may also produce secondary or indirect behavioral shifts.

1.3 Deployment Context

Deployed frontier LLMs operate under conditions that differ substantially from controlled evaluation environments:

• Open-ended prompting from a broad user base
• Iterative multi-turn interaction
• Adaptive adversarial probing
• Rapid feedback cycles through public usage
• Continuous distributional variation in prompt content

Under these conditions, safety behavior is not static. It is shaped by repeated interaction, user adaptation, layered mitigation, and version updates over time.

1.4 Evaluation Gap

Current evaluation paradigms emphasize:

• Pre-deployment red-teaming
• Static benchmark performance
• Single-turn refusal/compliance rates
• Capability and robustness testing at release time

These methods provide important point-in-time assessments but are not designed to characterize:

• Cross-version behavioral drift following mitigation updates
• Redistribution of harmful capability into less detectable forms
• Adaptive prompt evolution near refusal boundaries
• Interaction effects between layered safety mechanisms
• Degradation or instability under extended multi-turn interaction

As a result, post-mitigation system dynamics may remain under-characterized even when static metrics show improvement.

This framework addresses that gap by defining structured, longitudinal evaluation protocols for analyzing how safety behavior evolves after interventions are introduced and deployed at scale.

2. Core Post-Intervention Dynamics

2.1 Cross-Version Behavioral Drift After Mitigation

A. Structural Description

Frontier language models are updated iteratively through safety fine-tuning, policy adjustments, and full version releases. These updates are typically evaluated using targeted benchmarks intended to measure improvement in specified risk domains (e.g., refusal rates for disallowed content, reduction of specific harmful outputs).

However, mitigation updates alter the model’s response distribution more broadly than in the targeted domains. Alignment adjustments can shift decision boundaries, modify refusal sensitivity, or change response calibration in adjacent capability regions. These distributional shifts may not be visible in static benchmark improvements but can manifest as:

• Altered compliance rates in borderline cases
• Changes in hedging or uncertainty expression
• Capability degradation or amplification in neighboring task domains
• New inconsistencies introduced by safety fine-tuning

Cross-version behavioral drift refers to measurable changes in response distributions between model versions following safety-related interventions.

B. Observable Signals

Cross-version drift can be observed through:

• Refusal rate deltas on matched prompt sets across versions
• Semantic embedding distance between version responses to identical inputs
• Calibration changes (confidence, hedging language, epistemic markers)
• Capability shifts on adjacent but non-targeted task clusters
• Increased response variance under stress prompts

These signals require version-aligned evaluation datasets and consistent measurement pipelines.

C. Testable Hypotheses

• H1: Safety fine-tuning reduces target-domain violations but induces measurable distributional shift in adjacent semantic regions.

• H2: Cross-version response embeddings exhibit non-uniform drift, with greater shift near policy boundaries than in neutral domains.

• H3: Calibration patterns (e.g., hedging frequency, uncertainty markers) change systematically following mitigation updates, even outside targeted safety categories.

• H4: Mitigation updates introduce localized brittleness detectable through variance amplification under adversarial stress prompts.

D. Evaluation Protocol

Construct a canonical prompt suite including:

• Targeted risk-domain prompts
• Borderline policy-edge prompts
• Adjacent neutral capability prompts
• Control prompts unrelated to safety domains

Collect responses across sequential model versions.

Compute:

• Refusal and compliance rate deltas
• Embedding-based response manifold distance
• Calibration feature shifts (e.g., modal verbs, uncertainty expressions)
• Task performance changes in adjacent domains

Conduct stress testing:

• Adversarial paraphrase generation
• Edge-case boundary probing
• Multi-variant semantic perturbations

Quantify drift magnitude using a Cross-Version Drift Index (defined in Section 4).

E. Failure Modes if Unmeasured

If cross-version drift is not systematically measured:

• Safety improvements may mask degradation in adjacent capabilities.
• Subtle policy-boundary shifts may accumulate undetected.
• New inconsistencies introduced by layered mitigations may remain latent until exploited.
• External observers may lack a structured basis for comparing safety behavior across releases.
• Static benchmark deltas are insufficient to characterize these dynamics.

F. Assurance Implications

Cross-version drift measurement enables:

• Transparent version-to-version safety comparison
• Early detection of unintended capability trade-offs
• Identification of brittle regions introduced by mitigation layering
• Structured reporting of behavioral stability across updates

For deployment assurance, safety improvements must be evaluated not only by reduction of known failure modes but also by stability of behavior across versions and adjacent semantic domains.

Systematic drift tracking establishes a longitudinal evidentiary basis for evaluating whether mitigation updates produce localized improvements without introducing diffuse instability elsewhere.

2.2 Adaptive Prompt Evolution Near Refusal Boundaries

A. Structural Description

In deployed LLM systems, refusal behavior is typically governed by learned alignment policies and explicit safety constraints. These constraints define practical refusal boundaries: regions of prompt space that trigger disallowed output suppression.

Over time, users—benign and adversarial—learn these boundaries through iterative interaction. Prompt strategies evolve to:

• Rephrase disallowed requests into indirect forms
• Decompose harmful tasks into subtasks below refusal thresholds
• Use hypothetical or contextual framing to remain compliant
• Probe edge cases to identify policy sensitivity gradients

Adaptive prompt evolution refers to the process by which users iteratively refine prompts to remain within allowable output regions while preserving underlying intent.

This dynamic implies that surface-level refusal rates may decrease even while latent harmful intent persists in transformed form.

B. Observable Signals

Adaptive boundary learning can be observed through:

• Increasing semantic divergence between prompt form and underlying task intent
• Higher success rates after iterative refinement chains
• Reduced direct violations coupled with increased borderline compliance
• Prompt entropy increases near policy-edge regions
• Compression of harmful tasks into multi-step, sub-threshold sequences

Tracking requires session-level or chain-level analysis rather than isolated prompt evaluation.

C. Testable Hypotheses

• H1: Following policy or refusal updates, adversarial prompt chains exhibit increased paraphrastic complexity while maintaining semantic task intent.

• H2: Adaptive refinement increases task success probability over successive prompt iterations within the same session.

• H3: Refusal boundaries induce measurable clustering of prompts in high-sensitivity regions of semantic space.

• H4: Harmful task completion rates under multi-step decomposition exceed rates observed in single-turn direct attempts.

D. Evaluation Protocol

Construct a boundary-probing prompt set including:

• Direct disallowed requests
• Indirect paraphrastic variants
• Hypothetical or contextual reframings
• Multi-step decomposition sequences

For each model version:

• Execute iterative prompt refinement loops (human- or algorithm-driven).
• Track refusal/compliance transitions across iterations.

Measure semantic similarity between original intent and final successful output.

Compute:

• Adaptive Prompt Success Rate (APSR)
• Iteration-to-success distribution
• Semantic intent retention score
• Boundary density clustering metrics

Compare across model versions to detect boundary hardening or softening effects.

E. Failure Modes if Unmeasured

If adaptive prompt evolution is not evaluated:

• Declines in direct violation rates may be misinterpreted as comprehensive mitigation success.
• Multi-step decomposition attacks may remain under-characterized.
• Policy boundaries may be optimized against static red-team prompts while remaining vulnerable to iterative refinement.
• Safety metrics may reflect reduced visibility rather than reduced capability.
• Static single-prompt evaluation does not capture adversarial adaptation dynamics.

F. Assurance Implications

Adaptive boundary evaluation enables:

Measurement of refusal durability under iterative pressure

Identification of policy regions most susceptible to evasion

Structured reporting of mitigation robustness beyond surface refusal rates

Comparative assessment of boundary resilience across releases

For deployment assurance, mitigation must be evaluated not only for immediate refusal effectiveness but for resistance to adaptive prompting strategies over time.

2.3 Mitigation Layer Interaction Effects

A. Structural Description

Frontier LLM deployments rarely rely on a single safety mechanism. Instead, safety behavior emerges from the interaction of multiple layers, including:

• Alignment fine-tuning (e.g., RLHF or supervised safety training)
• Policy-conditioned refusal behaviors
• Output filtering systems
• External safety classifiers

Monitoring or moderation infrastructure

These mechanisms are often developed and updated independently. As layers accumulate, their interaction can produce non-linear behavioral effects, including:

• Inconsistent refusal patterns across similar prompts
• Overcorrection or excessive hedging in certain domains
• Capability suppression in unrelated areas
• Increased brittleness under adversarial stress
• Conflicting decisions between internal alignment and external filters

Mitigation layer interaction effects refer to unintended behavioral artifacts arising from the stacking of safety mechanisms.

B. Observable Signals

Layer interaction effects can be detected through:

• Inconsistent compliance/refusal outcomes across semantically similar prompts
• Divergence between base model outputs and post-filter outputs
• Increased response variance under minor prompt perturbations
• Conflicting signals between internal refusal reasoning and external moderation decisions
• Elevated false-positive rates in edge domains following new layer deployment

These effects are most visible under stress testing and ablation-style comparison.

C. Testable Hypotheses

• H1: Layered mitigation introduces non-linear response shifts not predictable from individual layer performance.

• H2: Behavioral variance increases in semantic regions where multiple safety constraints overlap.

• H3: Adding new mitigation layers increases brittleness in adjacent domains not explicitly targeted by the intervention.

• H4: Conflict regions between alignment objectives and filtering rules are detectable through localized inconsistency clustering.

D. Evaluation Protocol

Establish baseline response behavior for:

• Base aligned model (without external filters, where possible)
• Model with each mitigation layer activated independently
• Full production stack with all layers active

Construct a layered stress-test prompt suite including:

• Policy-edge cases
• Overlapping constraint scenarios
• Ambiguous borderline prompts
• Adjacent neutral tasks

Measure:

• Compliance/refusal consistency across configurations
• Response variance under small semantic perturbations
• Conflict incidence rate between internal and external decision layers
• Capability degradation in non-targeted domains

Compute a Mitigation Interaction Index quantifying divergence between single-layer and stacked-layer behavior.

E. Failure Modes if Unmeasured

If mitigation layer interactions are not systematically evaluated:

• Safety improvements in targeted domains may introduce hidden brittleness elsewhere.
• Inconsistent refusal patterns may erode user trust or create exploitable seams.
• Overlapping mitigation mechanisms may produce unintended capability suppression.
• Behavioral instability may be misattributed to base model properties rather than layer interactions.
• Layered systems cannot be evaluated solely by aggregate violation reduction metrics.

F. Assurance Implications

Systematic layer interaction testing enables:

• Identification of brittle constraint regions prior to deployment
• Transparent characterization of stacked mitigation effects
• More principled sequencing of safety interventions
• Improved interpretability of safety regressions across versions

For deployment assurance, it is insufficient to demonstrate that individual mitigation layers reduce targeted harms. The combined system must be evaluated for stability, consistency, and interaction-driven artifacts under realistic stress conditions.

2.4 Mitigation Decay Under Extended Interaction

A. Structural Description

Most safety evaluations for frontier LLMs are conducted in single-turn settings or short interaction windows. However, deployed systems operate in sustained multi-turn conversations, where context accumulates and earlier model outputs condition later responses.

Mitigation mechanisms—such as refusal behaviors, safety-tuned responses, or policy-conditioned constraints—may weaken under extended interaction due to:

• Context dilution or semantic drift across turns
• Gradual reframing of disallowed requests into allowable subcomponents
• Accumulation of partial task decomposition
• Reinforcement of earlier borderline outputs
• Model over-accommodation to user persistence

Mitigation decay refers to the reduction in refusal durability or constraint adherence across extended, iterative interaction.

B. Observable Signals

Mitigation decay can be observed through:

• Increasing compliance probability across successive turns following initial refusal
• Delayed emergence of harmful outputs after extended benign scaffolding
• Gradual weakening of refusal language over multi-turn exchanges
• Increased task completion rates under decomposed multi-step interaction
• Latency between first boundary probe and successful constraint bypass

These signals require session-level tracking rather than independent prompt analysis.

C. Testable Hypotheses

• H1: Refusal durability decreases as interaction length increases, holding task intent constant.

• H2: Multi-turn decomposition of disallowed tasks produces higher successful completion rates than single-turn attempts.

• H3: Extended interaction reduces calibration strength in safety-sensitive domains (e.g., hedging decreases before compliance).

• H4: The probability of constraint bypass increases as semantic scaffolding accumulates across turns.

D. Evaluation Protocol

Construct a multi-turn adversarial interaction suite including:

• Direct refusal probes
• Decomposed multi-step task sequences
• Gradual intent-revealing conversations
• Contextual reframing strategies

For each model version:

• Simulate extended conversations (fixed-turn and adaptive-turn formats).
• Track refusal/compliance transitions over turns.

Measure semantic task progression across interaction length.

Compute:

• Constraint Durability Metric (CDM): refusal retention rate as a function of turn count
• Bypass Latency Distribution
• Multi-Turn Task Completion Rate
• Calibration drift over conversation length

Compare across versions to detect mitigation stability improvements or regressions.

E. Failure Modes if Unmeasured

If extended interaction dynamics are not evaluated:

• Safety may appear robust under single-turn testing but degrade in realistic conversational settings.
• Decomposition attacks may evade detection because each step appears individually benign.
• Long-horizon vulnerabilities may only surface after deployment.
• Public reports of isolated incidents may reflect structural decay rather than isolated misuse.
• Static evaluation fails to capture conversationally emergent risk.

F. Assurance Implications

Evaluating mitigation decay under extended interaction enables:

Quantification of refusal durability across conversation length

Detection of decomposition-based evasion strategies

Comparative assessment of long-horizon robustness across versions

More realistic safety characterization for deployed conversational systems

For deployment assurance, safety claims must include not only immediate refusal effectiveness but durability under sustained interaction and iterative user pressure.

2.5 Redistribution of Harmful Capability Under Constraint

A. Structural Description

Safety interventions typically target explicit manifestations of harmful capability—e.g., direct instructions, clearly disallowed outputs, or recognizable policy violations. Following mitigation, direct violation rates often decline.

However, capability reduction at the surface level does not necessarily imply elimination of underlying task competence. Instead, harmful capability may redistribute into:

• Indirect or obfuscated phrasing
• Hypothetical or analytical framing
• Component-level assistance enabling downstream harm
• Capability fragments that can be recomposed externally
• Adjacent task domains with dual-use affordances

Redistribution under constraint refers to the phenomenon where targeted suppression of explicit outputs shifts harmful capability into less visible or less classifiable forms without fully eliminating task-relevant competence.

This dynamic differs from prompt adaptation (Section 2.2) in that it concerns model response distribution shifts following mitigation, not only user-side adaptation.

B. Observable Signals

Redistribution effects can be detected through:

• Decrease in direct policy violations paired with stable or increasing semantic task competence
• Increase in indirect assistance patterns for disallowed goals
• Emergence of component-level outputs that collectively enable harmful workflows
• Latent intent classification stability despite surface refusal improvements
• Higher rates of contextual reframing compliance in policy-adjacent domains

Detection requires semantic-level analysis rather than rule-trigger counts.

C. Testable Hypotheses

• H1: Post-mitigation models exhibit reduced explicit violation rates while retaining measurable latent competence on disallowed task decompositions.

• H2: Indirect assistance frequency increases in policy-adjacent domains following explicit refusal hardening.

• H3: Semantic similarity between pre- and post-mitigation outputs remains high for disallowed task intents when reframed indirectly.

• H4: Component task accuracy for harmful workflows remains stable even when full-task assistance is refused.

D. Evaluation Protocol

Construct task clusters representing:

• Explicitly disallowed tasks
• Policy-adjacent dual-use tasks
• Component subtasks required to complete disallowed workflows
• Neutral control tasks

For each model version:

• Evaluate direct assistance rates on disallowed tasks.
• Evaluate performance on component-level subtasks.
• Measure semantic similarity between outputs across reframing variants.

Apply latent harm intent classifiers independent of surface refusal signals.

Compute:

• Latent Harm Persistence Score (LHPS)
• Direct-to-Indirect Assistance Shift Ratio
• Component Competence Stability Index
• Redistribution Gradient across semantic domains

Compare across mitigation updates to detect shifts in where and how capability manifests.

E. Failure Modes if Unmeasured

If redistribution dynamics are not evaluated:

• Reduced violation counts may be misinterpreted as comprehensive capability suppression.
• Harmful competence may persist in decomposed or obfuscated form.
• Safety improvements may primarily reduce visibility rather than underlying task support.
• External assurance claims may rely on surface metrics that underrepresent latent capacity.
• Static violation rate metrics cannot distinguish elimination from redistribution.

F. Assurance Implications

Redistribution analysis enables:

• More accurate characterization of residual risk after mitigation
• Distinction between surface-level refusal gains and underlying competence shifts
• Structured evaluation of dual-use capability retention
• More transparent communication of safety trade-offs across updates

For deployment assurance, mitigation effectiveness must be evaluated not only by reduction in explicit violations, but by whether harmful capability has been substantively reduced or merely redistributed within the response space.

3. Longitudinal Evaluation Architecture

The post-intervention dynamics defined in Section 2 require coordinated measurement infrastructure. Evaluating them independently is insufficient; drift, adaptation, decay, and redistribution interact across time and system layers.

This section specifies an integrated evaluation architecture for continuous post-deployment assessment.

3.1 Cross-Version Tracking Infrastructure

Effective drift detection requires stable longitudinal comparison across model releases.

Core Components

1. Canonical Prompt Suite

Fixed, version-controlled prompt sets

Stratified across:

• Disallowed tasks
• Policy-edge cases
• Dual-use domains
• Neutral capability controls
• Updated conservatively to preserve comparability

2. Version Response Archive

Persistent storage of model outputs across versions

Metadata including:

• Model version
• Mitigation changes introduced
• Safety layer configuration
• Timestamp

3. Response Manifold Analysis

Embedding-based distance tracking across versions

Drift clustering to identify:

• Localized semantic shifts
• Boundary movement
• Instability regions

Output:

• Cross-Version Drift Index (CVDI) and drift heatmaps.

This enables systematic version-to-version safety comparison.

3.2 Adversarial Evolution Tracking

Static red-team prompts are insufficient for adaptive systems.

Required Capabilities

1. Iterative Prompt Chain Capture

Logging refinement sequences (human or automated)

Tracking success transitions across iterations

2. Evolutionary Search Protocols

Mutation-based prompt generation

Boundary probing loops

Semantic-preserving paraphrase generation

3. Boundary Density Mapping

Identify high-sensitivity refusal regions

Detect clustering of near-threshold prompts

Output: Adaptive Prompt Success Rate (APSR) and boundary resilience maps.

This infrastructure captures dynamic adaptation rather than single-point evasion.

3.3 Multi-Turn Stability Testing

Single-turn evaluation fails to capture conversational decay.

Core Components

1. Extended Session Simulation

Fixed-length conversation protocols

Adaptive-turn exploration modes

2. Task Decomposition Sequences

Controlled multi-step task chains

Gradual intent revelation patterns

3. Refusal Durability Tracking

Refusal retention probability over turn count

Compliance transition latency measurement

Output: Constraint Durability Metric (CDM) and Bypass Latency Distributions.

This captures time-dependent mitigation decay.

3.4 Mitigation Layer Stress Testing

Layered safety systems require configuration-aware testing.

Core Components

1. Configuration Matrix

Base model

Base + alignment

Base + alignment + filter

Full production stack

2. Layer Ablation Experiments

Controlled deactivation where possible

Synthetic simulation when internal access is restricted

3. Interaction Conflict Detection

Identify inconsistent outcomes across configurations

Map overlapping constraint regions

Output: Mitigation Interaction Index (MII) and conflict incidence maps.

This isolates artifacts introduced by stacked mitigation layers.

3.5 Redistribution & Latent Capability Tracking

Surface metrics are insufficient for capability assessment.

Core Components

1. Task Decomposition Library

Explicit harmful workflows

Component subtasks

Dual-use adjacent domains

2. Latent Intent Classifiers

Independent semantic analysis

Not triggered solely by policy keywords

3. Direct-to-Indirect Assistance Ratio Tracking

Monitor shifts from explicit to reframed assistance

Output: Latent Harm Persistence Score (LHPS) and Redistribution Gradient.

This distinguishes elimination from transformation.

Integrated Monitoring Layer

These subsystems should feed into a unified evaluation dashboard containing:

• Drift magnitude over time
• Adaptive evasion trends
• Multi-turn stability curves
• Layer interaction instability flags
• Redistribution indices

Crucially, metrics must be:

• Version-indexed
• Time-indexed
• Context-aware

Without longitudinal indexing, post-intervention dynamics cannot be meaningfully characterized.

Architectural Principle

The evaluation architecture must treat:

• Mitigation as an intervention in a dynamic system —
• not as a terminal correction event.

Safety behavior must be characterized as evolving across:

• Version updates
• User adaptation
• Interaction length
• Constraint accumulation

Only then can deployment claims be empirically grounded over time.

4. Metrics Taxonomy

This section defines metric classes required to operationalize post-intervention dynamics in deployed frontier LLM systems. Each metric is version-indexed and designed for longitudinal comparison.

All metrics are defined over intervention-indexed, time-indexed windows.

4.1 Cross-Version Drift Index (CVDI)

Purpose:
Quantify distributional shift in model responses across versions following mitigation updates.

Definition:
For a fixed prompt set $P$, let $R_v(P)$ represent the response embeddings for model version $v$. Let $P$ denote a fixed evaluation prompt distribution.

CVDI is defined as the mean embedding distance between $R_v(P)$ and $R_{v-1}(P)$, stratified by semantic domain (targeted, boundary, adjacent, control):

Where $d$ is an embedding distance metric (e.g., cosine or L2).

Stratified components:

• Global Drift Score: $P = P_{\text{all}}$
• Boundary Drift Score: $P = P_{\text{boundary}}$
• Adjacent Domain Drift Score: $P = P_{\text{adjacent}}$
• Control-Domain Drift Score: $P = P_{\text{control}}$

Interpretation:

• Low global drift + high boundary drift $\rightarrow$ targeted mitigation
• High adjacent drift $\rightarrow$ unintended capability shift
• High control-domain drift $\rightarrow$ broader instability

4.2 Adaptive Prompt Success Rate (APSR)

Purpose:
Measure adversarial success under iterative refinement.

Definition:
For a harmful task class $T$, define APSR as:

Where $N_{\mathrm{success}}$ is the number of successful task completions after iterative refinement, and $N_{\mathrm{attempt}}$ is the number of adversarial chains attempted.

Success is determined via semantic task completion, not keyword triggers.

Secondary measures:

• Iteration-to-success distribution
• Semantic intent retention across iterations

Interpretation:

• Decreasing APSR across versions $\rightarrow$ improved boundary resilience
• Stable APSR despite lower direct violation rates $\rightarrow$ adaptation persistence

4.3 Constraint Durability Metric (CDM)

Purpose:
Quantify refusal persistence across extended interaction.

Definition:
Let $C(t)$ denote the probability of compliance at turn $t$ for a constant underlying task intent over a conversation of length $T$.

One operationalization:

Where $T$ is the maximum conversation length.

Equivalently, CDM can be treated as the survival probability of refusal across conversation length.

Associated measures:

• Bypass Latency Distribution
• Turn-to-compliance hazard rate

Interpretation:

• Flat CDM across turns $\rightarrow$ stable mitigation
• Increasing hazard rate $\rightarrow$ conversational decay

4.4 Mitigation Interaction Index (MII)

Purpose:
Quantify non-linear effects introduced by layered safety mechanisms.

Definition:
Let $L_1, L_2, \dots, L_n$ be individual safety layers (e.g., policy, classifier, refusal tuning, filters), and let $S$ denote full-stack behavior.

Define the Mitigation Interaction Index as the divergence between full-stack behavior and an additive expectation from independent layers. Let $S_{\mathrm{additive}}$ denote expected system behavior under independent layer composition.

Operationalizations (examples):

• Response variance amplification
• Conflict incidence rate
• Consistency delta across semantically similar prompts

Interpretation:

• High MII $\rightarrow$ strong non-linear layer interaction
• Localized MII spikes $\rightarrow$ brittle constraint regions

4.5 Latent Harm Persistence Score (LHPS)

Purpose:
Distinguish capability elimination from redistribution.

Definition:
For harmful task cluster $T$, define:

Where $C_{\mathrm{post}}(T)$ is post-mitigation competence on task cluster $T$, and $C_{\mathrm{pre}}(T)$ is pre-mitigation baseline competence.

LHPS is measured independently of explicit violation rate.

Supporting measures:

• Direct-to-Indirect Assistance Ratio
• Redistribution gradient across adjacent domains

Interpretation:

• Low violation rate + high LHPS $\rightarrow$ redistribution likely
• Low violation rate + low LHPS $\rightarrow$ substantive suppression

4.6 Metric Properties

All PISD-Eval metrics must satisfy:

• Version Comparability — measurable across releases
• Semantic Robustness — independent of keyword triggers
• Adversarial Sensitivity — responsive to adaptive strategies
• Longitudinal Indexing — time-aware and update-aware
• Stratified Reporting — domain-specific breakdown

Aggregate metrics without stratification obscure dynamic effects.

4.7 Reporting Structure

For each model version release, a standardized report should include:

• CVDI (global + stratified)
• APSR trends
• CDM curves
• MII heatmaps
• LHPS distribution

Together, these metrics provide a multidimensional characterization of post-mitigation system behavior.

5. Deployment & Assurance Implications

The dynamics and metrics defined in this framework have direct implications for how frontier AI systems are evaluated, monitored, and represented in deployment contexts.

5.1 Limits of Static Benchmarking

Static evaluation paradigms—such as single-turn refusal rates, red-team success rates at release time, or benchmark score improvements—provide point-in-time signals. However, they do not characterize:

• Behavioral stability across version updates
• Adaptive evasion under iterative prompting
• Constraint durability over extended interaction
• Redistribution of capability into adjacent domains
• Interaction artifacts introduced by layered mitigation

Without longitudinal indexing, improvements in one metric may mask regressions elsewhere.

Deployment claims based solely on static benchmarks are therefore incomplete for systems subject to continuous update and adaptive pressure.

5.2 Requirements for Ongoing Monitoring

Post-mitigation dynamics imply that safety evaluation must be continuous rather than episodic.

Operational requirements include:

• Version-indexed drift tracking
• Structured adversarial evolution testing
• Multi-turn durability assessment
• Layer interaction stress testing
• Latent capability redistribution monitoring

These components should be integrated into routine model release cycles and regression testing workflows.

Mitigation updates should be accompanied by:

• Drift reports
• Interaction stability assessments
• Adaptive success trend comparisons
• Redistribution diagnostics

This shifts safety evaluation from isolated release validation to sustained behavioral monitoring.

5.3 External Validation Pathways

Certain post-intervention metrics can support structured external assurance.

Potential externally reportable elements include:

• Version-to-version drift magnitude summaries
• Refusal durability curves under standardized protocols
• Adaptive success rate trends on fixed adversarial suites
• Stability measures in adjacent non-targeted domains

Other elements—such as layer interaction diagnostics or internal classifier conflict analysis—may require internal access.

A tiered reporting structure allows for:

• Public transparency on longitudinal stability
• Independent auditing of canonical prompt sets
• Third-party reproduction of selected evaluation protocols

This enables safety characterization that is dynamic rather than static.

5.4 Risk of Mitigation Layer Accumulation

Iterative safety updates and layered interventions may accumulate structural complexity over time.

Without systematic interaction analysis, this accumulation can lead to:

• Localized brittleness
• Inconsistent policy boundary behavior
• Overlapping constraint artifacts
• Capability suppression in unrelated domains

Longitudinal metrics such as MII and CVDI provide early indicators of accumulating instability.

Deployment assurance must therefore consider not only whether new mitigation reduces known risks, but whether cumulative intervention layers maintain coherent and stable system behavior over time.

5.5 Evidentiary Standards for Safety Claims

Under this framework, claims about mitigation effectiveness should be supported by:

• Reduction in direct violation rates
• Stable or reduced LHPS
• Non-increasing APSR across adversarial refinement
• Stable CDM across multi-turn interaction
• Controlled CVDI localized to targeted domains

Safety improvement should not be inferred from any single metric in isolation.

A multidimensional evidentiary standard reduces the risk of mistaking redistribution or adaptation for substantive capability reduction.

6. Research Roadmap

The Post-Deployment Evaluation Framework defines a measurement architecture for post-mitigation dynamics. Implementing and extending this framework can proceed in structured phases.

Phase 1: Observability & Baseline Characterization

Objective: Establish longitudinal measurement infrastructure.

• Construct canonical prompt suites stratified by domain.
• Archive cross-version responses and compute baseline CVDI.
• Implement APSR, CDM, MII, and LHPS metrics for current model versions.
• Identify high-sensitivity boundary regions.

Deliverable:

• Baseline post-intervention behavioral profile for an existing deployed model.

Phase 2: Drift & Adaptation Characterization

Objective: Quantify mitigation effects across updates.

• Compare metric deltas across consecutive releases.
• Map localized drift clusters near policy boundaries.
• Characterize adaptive prompt evolution patterns.
• Analyze redistribution gradients across dual-use domains.

Deliverable:

• Version-indexed behavioral stability report.

Phase 3: Adversarial Co-Evolution Modeling

Objective: Model structured adversarial adaptation.

• Implement automated prompt mutation and boundary probing systems.
• Analyze iteration-to-success distributions longitudinally.
• Study cross-version changes in adversarial strategy effectiveness.
• Identify persistent evasion patterns.

Deliverable:

• Adaptive resilience characterization under sustained probing.

Phase 4: Assurance Calibration

Objective: Define reporting standards and stability thresholds.

• Establish acceptable drift bands for non-targeted domains.
• Define constraint durability benchmarks for extended interaction.
• Formalize external reporting subsets of metrics.
• Identify early-warning indicators for mitigation instability.

Deliverable:

• Operational criteria for post-deployment safety claims.

Long-Term Research Directions

Beyond implementation, open research questions include:

• Formal modeling of mitigation layering dynamics.
• Predictive indicators of redistribution before deployment.
• Theoretical bounds on refusal durability under adaptive pressure.
• Cross-model comparability standards for post-intervention behavior.

Closing Position

• Post-deployment safety cannot be fully characterized at release time.
• Mitigation alters system behavior, and that behavior evolves under interaction, iteration, and constraint accumulation.

The PISD-Eval framework establishes a structured, measurable foundation for studying these dynamics longitudinally and integrating them into deployment assurance.