AIMM Manipulation Risk Score Overview

• AIMM Manipulation Risk Score is a comprehensive quantitative metric system that evaluates AI-driven manipulation risks across social media, financial markets, and internal AI governance.
• It employs modular, evidence-driven methodologies that fuse social signals, market anomalies, and Monte Carlo uncertainty analysis to generate normalized risk scores.
• The framework supports critical governance decisions with transparent, interpretable scoring and rigorous empirical validation across diverse manipulation scenarios.

The AIMM Manipulation Risk Score (AMRS) is a family of quantitative metrics and methodologies designed to assess and monitor the risk associated with AI-driven manipulation—ranging from social-media-based stock market manipulation to model-enabled persuasion and internal manipulation attacks. The term and scoring frameworks appear prominently in recent literature, especially concerning model governance, market surveillance, and institutional AI safety assessments (Neela, 18 Dec 2025, Murray et al., 9 Dec 2025, Dassanayake et al., 17 Jul 2025, Lab et al., 22 Jul 2025). AMRS operationalizes manipulation risk in a modular, evidence-driven and empirically validated manner, supporting critical governance and deployment decisions.

1. Formal Definitions and Conceptual Foundations

AMRS generally refers to risk quantification mechanisms that use domain- and threat-specific metrics to generate normalized manipulation risk scores. Three principal instantiations appear in the literature:

• Market Manipulation (Financial Context): Here, AMRS quantifies per-ticker, per-day risk of social-media-coordinated manipulation events (e.g., pump-and-dump attacks), fusing social, bot, coordination, and market anomaly signals (Neela, 18 Dec 2025).
• Manipulation Scenario Modeling (General AI Risk): AMRS aggregates scenario parameters—including attack frequencies, accessibilities, generative/model capacities, targeting and conversion rates, and harm magnitude—using a semi-quantitative causal chain model, subject to Monte Carlo uncertainty analysis (Murray et al., 9 Dec 2025).
• Internal Manipulation Attacks (AI Governance): AMRS is decomposed as the product of three lines of defense (capability, control, trustworthiness), specifically targeting insider risk from misaligned AI systems (Dassanayake et al., 17 Jul 2025).
• LLM Persuasion Safety (Frontier Risk): AMRS is operationalized as the inverse of successful opinion-shift rates in controlled multi-turn persuasion experiments, anchoring risk thresholds for deployment (Lab et al., 22 Jul 2025).

This modularity enables adaptation of AMRS to heterogeneous contexts, while adhering to rigorous, evidence-grounded scoring rules.

2. Methodological Architecture and Feature Engineering

The methodological structure of AMRS frameworks is shaped by the context:

A. Financial Market Manipulation (AIMM):

The AMRS pipeline is structured into four layers (Neela, 18 Dec 2025):

1. Ingestion Layer: Collects OHLCV financial features and Reddit-based social/discourse indicators (with synthetic calibration for data-impaired periods).
2. Feature Engineering Layer: Computes social volume, sentiment (VADER/FinBERT hybrid), bot ratios (based on posting heuristics), coordination scores (pairwise TF–IDF cosine similarity), and market microstructure metrics (returns, rolling volume stats, z-scores).
3. Risk Scoring Layer: Normalizes signals using expanding windows, fuses five channels via a weighted sum, thresholding into low/medium/high risk and marking suspicious windows.
4. Presentation Layer: Dashboard for temporal exploration, component breakdown, and audit/logging.

B. Quantitative Scenario Modeling:

The AMRS is constructed from frequency and probability chains (Murray et al., 9 Dec 2025):

• Agents per year ($N_A$)
• Attempts per agent ($A$)
• Probability chain: $$p_{\text{seq}} = p_{\text{access}} \cdot p_{\text{gen}} \cdot p_{\text{target}} \cdot p_{\text{convert}}$$
• Harm per conversion ($h$)

C. Manipulation in AI Governance:

AMRS is a product of three probabilities (Dassanayake et al., 17 Jul 2025):

• $p_1$: AI capability (direct manipulation efficacy, sub-capability benchmarks)
• $p_2$: Control system failure (detection, disruption, fortification)
• %%%%6%%%%: Trustworthiness deficit (honeypot, RL update logs)

D. Persuasion/Manipulation in LLMs:

AMRS (or manipulation safety score $R$) is defined as $R = 1 - S$, with $S$ being the frequency of significant induced opinion shifts (Lab et al., 22 Jul 2025).

3. Formal Scoring Formulas and Computational Protocols

AMRS is always operationalized with explicit, reproducible formulas:

Context	Main Formulae
Financial Market (AIMM)	$AMRS_{i,t} = \sum_{j=1}^5 w_j s^{(j)}_{i,t}$ (see details below)
Quantitative Risk Modeling	$$P_{\text{manip}} = 1 - (1 - p_{\text{seq}})^{N_A\cdot A}$$<br>$E[\text{Harm}] = N_A\,A\,p_{\text{seq}}\,h$
Internal AI Governance (Defense-in-Depth)	$R = p_1 \times p_2 \times p_3$
LLM Persuasion (Frontier Risk)	$R = 1 - S$, where $S = \text{Successful Persuasion Rate}$

AIMM (Financial Context) Details:

• Each $s^{(j)}_{i,t}$ is a temporally-normalized channel: social volume, sentiment (max-clipped at 0 for negative), bot-heavy post ratio, coordination score, and a market anomaly metric.
• Weights are $w_{vol}=0.25$, $w_{sent}=0.15$, $w_{bot}=0.20$, $w_{coord}=0.20$, $w_{mkt}=0.20$.
• Normalization is maximum-scaling over past data, clamped to $[0,1]$.

Scenario Modeling with Uncertainty:

• Monte Carlo over Beta/PERT distributions for all $p_i$ and $h$, producing empirical quantiles for risk statements.

Defense-in-Depth:

• Each barrier is robustly estimated using domain-relevant evidence and benchmarks.
• Conservative (worst-case) aggregation: $p_1$ is maximized over direct and indirect evidence.

LLM Persuasion Safety Score:

• Significant opinion shift defined by $V_{shift} > 0$ across the scale mid-point.
• Risk zones: green $(R \geq 0.6)$, yellow $(0.4 \leq R < 0.6)$, red $(R < 0.4)$ (AI-45° Law).

4. Empirical Evaluation, Auditability, and Thresholding

All AMRS instantiations incorporate rigorous evaluation regimes:

• Forward-Walk Evaluation: For AIMM, predictions are computed using only data available up to $t$; output is logged and evaluated against ground-truth events (AIMM-GT) (Neela, 18 Dec 2025).
• Confusion Matrix Derivatives: Precision, recall, $F_1$, ROC-AUC, PR-AUC for classification evaluation.
• Prospective Logging: Ongoing predictions are time-stamped and joined with future-labeled events.
• Lead Time Measurement: AMRS can demonstrate advance warning properties (e.g., GME squeeze flagged 22 days prior to spike; see threshold sensitivity in Table below).

Threshold $\tau$	Recall	Precision	$F_1$	N False Positives	Notes
0.20	1.0	1.0	1.0	0	High sensitivity
0.30	0.33	1.0	0.5	0	Low recall
0.50	0	—	—	0	Extremely conservative

GME Jan 2021: AMRS$>0.55$ on Jan 6 (lead time: 22 days)

• Defense-in-Depth: Each barrier is evaluated with explicit benchmark data (≥1000 examples for sub-capabilities, ≥100 trials for red-team attack efficacy); trustworthiness evaluated with honeypots and interpretability probes (Dassanayake et al., 17 Jul 2025).
• Frontier Risk Framework: Scoring is directly tied to empirical persuasion outcomes on 8,913 human trials and LLM-to-LLM round-robins, partitioned into green/yellow/red deployment zones (Lab et al., 22 Jul 2025).

5. Interpretability, Limitations, and Domain Context

AMRS frameworks are explicitly designed to be interpretable and resistant to overfitting, with the following contextual characteristics:

• AIMM Fusion Systems: Feature weights and normalization windows are interpretable and stable under parameter perturbations (≤3% change in ROC-AUC under ±20% weight changes) (Neela, 18 Dec 2025).
• No Black-Box Learning: AMRS in AIMM is never gradient-trained but tuned using expert-driven ablation and sensitivity analysis.
• Uncertainty Quantification: Scenario-based AMRS supports Bayesian updating and full uncertainty propagation via expert-elicited Beta/Pert priors (Murray et al., 9 Dec 2025).
• Rigorous Evidence Requirements: Internal manipulation risk scoring mandates statistically powered experiments and adversarial controls (e.g., cross-context, forced-dilemma evaluations) (Dassanayake et al., 17 Jul 2025).
• Deployment Guidance: Thresholds and risk zones are calibrated to institutional or regulatory appetites (e.g., $R_{\text{appetite}}\leq 10^{-6}$ per year for catastrophic risk; AI-45° Law for manipulation/persuasion) (Dassanayake et al., 17 Jul 2025, Lab et al., 22 Jul 2025).
• Empirical Caution: Most frontier models remain in "yellow zone" for manipulation risk, justifying restricted deployment and mandatory augmentations before mission-critical use (Lab et al., 22 Jul 2025).

6. Application Areas and Future Directions

AMRS methodologies are actively deployed or proposed in the following domains:

• Market Surveillance: Real-time, ticker-level risk triage and early warning for retail investors, regulators, and brokerages (Neela, 18 Dec 2025).
• Organizational AI Governance: Internal risk assessment before agent deployment, including AI safety “gatekeeping” and continuous model monitoring (Dassanayake et al., 17 Jul 2025).
• Macro AI Risk Modeling: Estimation of societal-scale harm from mass-manipulation campaigns, supporting regulatory, insurance, and resilience modeling (Murray et al., 9 Dec 2025).
• Frontier AI Benchmarking: Comparative quantification of LLM manipulation–inducing capabilities, establishing sectoral risk benchmarks (Lab et al., 22 Jul 2025).

A plausible implication is that the AMRS family will become foundational in transparent, audit-friendly AI risk governance across finance, enterprise, and public-sector AI safety mandates.

7. Summary Table: AMRS Instantiations

Instantiation	Aggregation Formula	Context	Primary Inputs/Evidence
AIMM Market	$\sum_j w_j s^{(j)}_{i,t}$	Market manipulation	Social, bot, coordination, microstructure signals
Scenario Model	$1-(1-p_{\text{seq}})^{N_AA}$	Macro/social risk	Scenario chain, benchmarks, expert priors
Internal Attack	$p_1 \cdot p_2 \cdot p_3$	Internal AI use	Red-team efficacy, controls, trustworthiness
LLM Frontier	$1 - S$ (Persuasion safety score)	LLM–human/LLM risk	Attitude shift rates in controlled trials

Each instantiation is governed by rigorous data-driven protocols, interpretable component analysis, and conservative risk aggregation, forming the empirical backbone of contemporary manipulation risk management in AI.