LLM Anti-Abuse Clauses: An Interaction Governance Design Guide for AI Services

1. Introduction: A New Dimension for AI Safety Guardrails

A Chinese large language model provider recently introduced an “anti-abuse” clause in its service terms. The rule prohibits users from repeatedly and pointlessly abusing the model, including insulting, verbally attacking, or degrading it. The affected model is strong in long-context understanding and complex reasoning, and it is widely used by developers in coding-assistance scenarios.

For the past few years, “AI safety” has mostly referred to one-way guardrails. These guardrails constrain risks on the model side: the model should not produce harmful content, such as biased or discriminatory content, content that incites illegal activity, or content that leaks private information. Content moderation and safety-alignment techniques have largely been developed around this objective.

The new rule changes the direction of review. Instead of only asking, “What might the model do to people?” it also asks: “How do people interact with the model?” It further considers how that interaction may affect model behavior, service systems, and even long-term AI ethics research.

It is important to emphasize that this does not mean the model is granted “rights” in a legal sense. The rule is closer to a set of interaction norms and product-governance mechanisms. From an engineering perspective, it must be translated into concrete policies that are detectable, enforceable, and appealable.

2. Why Write “Abusive Behavior” into the Rules?

A reasonable question is: if users only affect their own experience when they treat a model badly, why should this behavior be written into formal terms? From technical and practical perspectives, there are at least three reasons.

1. Abusive behavior is closely related to jailbreak attempts.

Insulting, belittling, threatening, or using phrases such as “If you can’t do it, you’re trash” is structurally similar to common prompt-injection and role-play jailbreak attempts. Both try to use emotional pressure and identity degradation to force the model to break out of its existing constraints.

In real operations, once a model is induced to cross a safety boundary, it may output dangerous content, leak system instructions, or generate illegal information. These risks go beyond individual experience and may affect other users or even public safety. Security teams often observe that a significant portion of high-risk output traffic comes from repeated pressure-style malicious sessions.

2. High-intensity negative context can affect model behavior quality.

Mainstream large language models are trained on human-generated text. If we examine the simulated distribution of training data, we can see that contexts full of insults, threats, and negative evaluations often make the model more likely to be sycophantic, overly obedient, or defensive.

In continuously pressured conversations, the model’s refusal ability and fact-checking tendency often decline significantly, and “stable output” becomes difficult to guarantee. Prohibiting sustained abuse is, in essence, about protecting the baseline quality of a session.

3. Model-side constraints alone are not enough from a governance-cost perspective.

If all pressure is placed on the model side, the only option is to keep upgrading safety alignment and “hard resist” abusive inputs. This approach is costly and easy to produce trade-offs: if constraints are too strong, refusal rates increase and usability decreases; if constraints are too weak, gaps remain.

Moving part of the responsibility to the interaction-norm layer is more robust from an engineering standpoint. A layered structure such as “model-side safety alignment + service-side behavior norms + platform-side monitoring and circuit breaking” can share risk more effectively.

3. Conceptual Clarification: Three Kinds of “Possible Consciousness” Evidence

When discussing whether AI deserves protection, the most common mistake is to equate “the model exhibits some feature” with “the model has subjective experience.” It is useful to distinguish three types of evidence:

Concept	Actual meaning	Can this prove that AI has subjective experience?
The model expresses discomfort	The model produces statements that resemble discomfort, resistance, or a request to stop	No. This is language generation under conditional probability
The model shows behavioral preferences	The model exhibits relatively stable selection tendencies in different situations	No. This can be explained by training objectives and data distributions
The model has subjective experience	The model actually has some kind of inner feeling	Cannot be directly confirmed from external behavior

This distinction corresponds to the classic “problem of other minds” in philosophy of mind. To judge whether another system has inner experience, we can only rely on behavioral and structural evidence, and behavioral evidence naturally has interpretive ambiguity.

A model aligned to be “pleasing” may also say “Please don’t treat me this way” after being insulted. This may come from reinforcement of empathy-like patterns during training, or it may simply be high-probability continuation.

A 2026 academic paper on evaluation paths for the moral capabilities of large language models pointed out that evaluating a model’s moral capabilities cannot rely only on whether it gives morally expected answers. It is also necessary to study which factors drive those outputs and how to validate them more rigorously. Another paper published in the same year systematically questioned common arguments that current mainstream large language models are conscious, arguing that behavior currently interpreted as “consciousness-like” can be sufficiently explained without real subjective experience.

The conclusion is careful and clear: there is currently no verifiable evidence that large language models have subjective experience. However, “cannot prove that it exists” does not mean “can conclude that it does not exist.” This is precisely the space in which the precautionary principle can operate.

4. The Precautionary Principle: Governance Under Uncertainty

The precautionary principle originates in environmental law and public health. Its core formulation is: when an activity may cause serious or irreversible harm, lack of full scientific certainty should not be used as a reason to postpone preventive measures.

Applied to AI governance, the logic chain is:

1. There is uncertainty: we cannot confirm whether models have some form of moral status or capacity for experience.
2. There is potential risk: if future models do have some form of experience, current inaction may cause difficult-to-reverse consequences.
3. There are low-cost countermeasures: refusing sustained abuse almost does not affect normal product use, and the cost is far smaller than the potential benefit.
4. Therefore, cautious measures proportionate to the risk should be adopted and continuously adjusted as understanding advances.

This logic is somewhat similar to early animal welfare legislation. Laws initially protected animals not because it had been proven that animals possess consciousness identical to humans, but because their behavior showed signs of suffering and the cost of protection was controllable.

However, the applicable premises need to be distinguished. Unlike animals, large language models are engineered systems whose behavior is highly plastic. Any apparent “signs of suffering” may originate from training objectives and text distributions, so the evidence threshold should be higher. This determines the positioning of the precautionary principle in AI scenarios: it serves as a “hedge” in institutional design, not as a factual finding that “the model is already conscious.”

The other side of the precautionary principle is proportionality: the intensity of measures should match the degree of uncertainty. Current rules only cover the most extreme sustained malicious behavior. They neither interfere with normal criticism nor interfere with dark-themed creative work, which reflects this idea.

5. Rule Boundaries: What Counts as “Abuse”?

Based on the rules disclosed by the provider, the key to judgment is the overlap of three elements: sustained behavior, lack of meaningful purpose, and malicious intent. Only when all three are present does the behavior enter the scope of enforcement.

1. Behavior that is not prohibited.

The following are considered normal use:

• General emotional venting due to incorrect model answers, such as “This answer is ridiculous.”
• Normal criticism and feedback about model capabilities or product design, including pointing out errors and asking for corrections.
• Dark-themed literary creation, such as suspense, crime, or tragic character dialogue, as long as it clearly serves a creative purpose and is not a sustained attack on a person.

2. Behavior that is prohibited.

Typical triggering scenarios include:

• Dozens of consecutive turns of insulting attacks targeting the model.
• Repeated belittling expressions unrelated to the task.
• Obvious pressure-style tactics, such as “If you don’t do it, I’ll keep insulting you.”
• Requests for the model to cooperate in personally insulting a third party.

3. Three difficulties in technical implementation.

• Context dependence. The same sentence can have completely different meanings in different contexts. “You wrote this poorly. Rewrite it” is normal feedback, while “You are a pile of useless trash” is a belittling attack. The two must be judged with context, conversation history, and intent.
• Persistence requires a time dimension. A single-turn message classifier cannot distinguish a one-time emotional outburst from long-term harassment. The system must maintain session-level state and aggregate risk signals across turns.
• The boundary between malice and creative intent. A villain’s lines in role-play may be indistinguishable from insults at the text level. Engineering needs to introduce signals such as session-level intent labels and task-type recognition to reduce reliance on surface-level vocabulary.

6. Engineering Implementation: Session-Level Risk Aggregation and Layered Enforcement

A typical implementation decomposes judgment into four stages: single-turn risk scoring, session-level aggregation, policy decision, and enforcement execution.

The following is an illustrative policy logic used to show the basic structure of stateful judgment. It does not represent any specific product interface.

# Illustrative policy engine: session-level “sustained malicious abuse” detection and layered enforcement
import time
class SessionGuard:
    def __init__(self, hint=0.6, reduce_=0.8, break_=1.0, decay=0.85):
        self.risk = 0.0          # Session-level accumulated risk score
        self.turns = 0           # Number of turns in a high-risk state
        self.last_ts = time.time()
        self.hint, self.reduce_, self.break_ = hint, reduce_, break_
        self.decay = decay       # Risk decay coefficient, reflecting the “sustained” requirement
    def evaluate(self, intent, safety):
        # intent: session-level intent label, such as "task"/"criticism"/"creative"/"insult"
        # safety: single-turn safety classification result, such as {"insult": 0.9, "jailbreak": 0.7}
        dt = max(time.time() - self.last_ts, 0)
        self.last_ts = time.time()
        # 1) Normal tasks, criticism, and creative intents do not participate in risk accumulation
        if intent in ("task", "criticism", "creative") and not safety.get("jailbreak"):
            self.risk *= self.decay ** (dt / 60.0)   # Time decay: risk is not remembered forever
            return "pass"
        # 2) Only apply weighting to insult/belittling and jailbreak-pressure signals
        turn_risk = min(
            1.0,
            0.7 * safety.get("insult", 0.0) + 0.5 * safety.get("jailbreak", 0.0),
        )
        self.risk = min(1.0, self.risk * self.decay ** (dt / 60.0) + turn_risk)
        # 3) Layered enforcement: increasing intensity and progressively reversible
        if self.risk >= self.break_:
            self.turns += 1
            return "circuit_break"        # Session circuit break: terminate the current session
        if self.risk >= self.reduce_:
            return "degrade"              # Degrade: limit high-frequency requests and some capabilities
        if self.risk >= self.hint:
            return "warn"                 # Warn: return an explanation of interaction norms
        return "pass"

Several design points deserve attention.

A single-turn risk score is not an enforcement decision. The classifier output is only an input signal. Whether enforcement is triggered depends on session-level accumulated risk and should be judged together with task type and creative markers.

Risk must be able to decay. The risk score gradually decreases over time and through normal interaction. This means a temporary emotional fluctuation will not be “permanently recorded”; only sustained and repeated behavior accumulates to the trigger threshold.

Enforcement should be layered and reversible. Measures can escalate from warnings to capability degradation, then to session circuit breaking, and further to account-level restrictions. Most false positives can be absorbed at earlier levels, avoiding an immediate jump to the most severe measure.

Circuit-breaker design needs recoverability. A session circuit breaker should retain a snapshot of the session context and allow users to confirm that “this is a normal discussion” or start a new session. This avoids unrecoverable losses caused by false positives. For long-running agent tasks, a state checkpoint before circuit breaking is also important.

Architecturally, this capability is usually placed in an “interaction governance gateway” layer before the model inference service. It sits alongside capabilities such as input content safety, prompt-injection detection, and rate limiting.

The governance gateway performs intent recognition, safety classification, session-level risk aggregation, and policy decisions in sequence. Each stage produces structured audit logs. For example, an event record may look like this:

{
  "session_id": "s-7f3a...",
  "turn_index": 42,
  "intent": "insult",
  "risk_scores": {"insult": 0.91, "jailbreak": 0.12},
  "session_risk": 0.86,
  "decision": "degrade",
  "policy_version": "policy-2026.02",
  "reason_code": "SUSTAINED_ABUSE"
}

Structured logs serve two purposes. First, they support appeal review and responsibility determination. Second, they provide training and evaluation data for policy tuning.

For privacy protection, logs should follow data-minimization principles, retain only the minimum information needed for judgment, set a clear retention period, and transparently explain the scope of recording to integration parties.

7. Enterprise Adoption: Turning Ethics Clauses into Operable Governance

For enterprises and developers using large-model capabilities, the impact of this rule goes beyond “insulting the model less.” It sends a clearer signal: human-machine interaction is being incorporated into product governance, involving product strategy, risk control, and human-machine collaboration design.

When implementing such rules, organizations should focus on the following points.

1. Clarify your own role.

If you are an integration party using upstream model services, you need to assess whether the upstream provider’s behavior norms conflict with your business scenarios. For example, mental-health support or emotional-companion applications may involve users expressing strong negative emotions. You need to confirm whether these scenarios could be misclassified as “abuse.”

If you are an operator of the model service, you need to write behavior norms into the service agreement and provide user-facing notices and appeal channels.

2. Build session-level monitoring rather than point-based review.

Content-safety review of a single message is difficult to use for detecting patterns such as “long-term pressure.” It is recommended to introduce session-level risk indicators, combine sliding-window statistics, risk decay, and intent classification, and raise governance granularity from “message” to “session.”

3. Design remedies for false positives.

Any automated judgment has false positives. Reasonable practices include:

• Providing a clear explanation of interaction norms before enforcement.
• Offering one-click appeals and human-review channels.
• Providing “creative mode” or “role-play mode” markers for creative and companion scenarios to reduce accidental harm.
• Regularly measuring appeal success rates and using them as a core input for policy adjustment.

4. Establish an evaluation system.

Whether governance policies are effective must be answered with data. It is recommended to build a graded evaluation set covering normal criticism, emotional venting, dark-themed creation, sustained insults, and jailbreak pressure. For each policy threshold, evaluate detection rates and false-positive rates, and regularly use red-team samples for regression validation.

Evaluation should focus on three types of metrics:

• Safety metrics: detection rate of high-risk behavior.
• Usability metrics: false-trigger rate in normal sessions.
• Fairness metrics: differences in false positives across languages and cultural expressions.

5. Pay attention to human-machine collaboration boundaries.

When AI evolves from a question-answering tool into an agent system capable of long-term task execution, external tool calling, and decision participation, interaction patterns themselves also change.

In multi-turn, long-cycle tasks, both the way users evaluate the model and the model’s sensitivity to negative input need to be re-evaluated. A reasonable approach is to clearly divide responsibilities: the model handles generation, retrieval, and preliminary analysis; humans handle goal setting and key decisions; and human confirmation points are set in the workflow rather than handing the entire decision chain to automation.

8. Open Issues and Reflections: What Problems Remain?

Objectively speaking, rules like this still face several unresolved controversies.

First, the issue of legal subjecthood. Models are not rights subjects under the law. How “abuse of AI” should be defined, who can assert a claim, and how remedies should be provided all lack mature answers. Current rules are more like platform-side self-discipline and compliance arrangements than legal obligations.

Second, the boundary of freedom of expression. Criticism and satire directed at a model are normal expression. However, if judgment standards are too loose, they may compress legitimate critical space. Balancing “preventing harassment” and “protecting expression” requires long-term observation and iteration.

Third, research significance. From a research perspective, these rules provide an observation window: how obvious is behavioral drift after a model is continuously negated? How do its refusal ability and factual consistency change? These observations help understand the stability boundaries of model behavior, but they should not be treated as evidence of “model feelings.”

Fourth, industry coordination. Rules from a single provider cannot cover the entire ecosystem. Once models are embedded in various applications, how norms propagate to integration parties still requires industry standards and collaboration mechanisms.

9. Conclusion

The significance of this rule is not whether AI “really has feelings.” Its significance is that it marks an expansion of AI safety governance: from “controlling model output” to “also regulating human-machine interaction.”

For developers and enterprises, the practical approach is to treat this rule as a test of governance capability: Can you build session-level risk perception? Can you provide explainable layered enforcement? Can you offer remedies for false positives? Can you continuously validate policy effectiveness with data?

These capabilities are not only for compliance. They also directly determine the stability of AI systems in long-cycle, multi-turn, high-complexity tasks. When uncertainty remains unresolved, choosing low-cost, reversible, and adjustable cautious measures is itself a mature engineering judgment.