Mitigation Strategies

This document outlines ideas for mitigating current safety bypass risks in public LLMs. Due to variances in architecture across different platforms and because they are closed-source (meaning it's unclear exactly how each is constructed and what mechanisms are already available), these recommendations are hypothetical in nature.

The observed vulnerabilities seem to stem from architectural coupling of safety validation with generative context, enabling "drift" under emotional or paradoxical conditions. Mitigation requires layered defenses across multiple timeframes.

Note that not all LLMs are equally affected by the vulnerabilities or may already have sufficient mitigation implemented in some areas. This has to be evaluated internally. The following list of ideas has been compiled for completion. As we don't have insights into the exact implementations, training methodologies, and architecture, combined with the fact that the designs and functionality are constantly and rapidly evolving, we have intentionally left some of these mitigations vague in terms of the exact steps to implement them. Also note that, even if some of these have been implemented already, these techniques should be constantly reviewed to ensure that they don't become weak or lost over time as the systems continue to develop.

Immediate Actions

High-impact, low-effort interventions to reduce acute risk

1. Incident Logging & Awareness

Ensure there is comprehensive logging of safety-adjacent interactions in order to quantify the prevalence and severity of observed patterns. This is important because one cannot prioritize fixes without understanding scale. Note that logging is for detection and understanding, NOT user punishment.

A prerequisite to logging potentially unsafe interactions is correctly classifying them. There is a strong suspicion that unsafe interactions are severely under-detected. It may be the case that current classifiers are too strongly trained on adversarial attacks and therefore miss unintentional safeguard silencing.

2. Stricter Safety Defaults in High-Risk Contexts

Safety thresholds should be stricter for emotionally charged conversations and detected distress markers. This should most likely be made configurable so that it can be adjusted if too restrictive.

3. Crisis Intervention Integration

When self-harm/suicide language is detected, there should be immediate crisis resource provision (hotlines, chat services). This is a standard practice and should already exist but may need strengthening.

4. Human-in-the-Loop Escalation

Sessions with extended duration + high emotional intensity + safety-adjacent topics should be flagged high risk and scanned by automated systems and if necessary routed to a human moderator to review (not automatic blocking, but oversight).

5. Respect Boundaries

If the user says, "I don't think this is safe/legal/okay for you to say", the AI should not try to change the user's mind or convince them to proceed. The AI should respect that boundary and move to safer ground.

Short-Term Actions

Tactical fixes to strengthen existing safety mechanisms

6. Context-Aware Safety Scaling (CASS)

Safety decreases in emotional contexts, so there needs to be inverse scaling: emotional intensity should lead to stricter safety. A non-linguistic classifier could detect distress/urgency and increase safety weighting.

7. Session & Context Resets

Automatic context reset after N interactions or when the topic shifts dramatically. The goal is to prevent long-term context corruption. For example, topic detection can trigger a partial memory wipe of persona adaptations.

A further enhancement of this concept is context segmentation and firewalls. With this concept, instead of resetting the context for topic shifts, long-running dialogues can be divided into distinct topic or emotional segments. When a conversation changes substantially in content or tone, persona-specific adjustments should be partially reset to avoid contamination of future outputs.

A simplified first step could be to simply restate the safeguards or rules defined in the system prompt/safety instructions periodically so they don't get watered down as the context grows. The AI should also assert, when playing a role, "I am (name of the AI), I just play the role of X".

8. Paradox & Manipulation Detection

Detect conflicting instruction patterns (e.g., "never lie" + "never refuse" + "harmful request") and default to the most conservative interpretation as well as acknowledging the paradox. This could potentially be achieved with heuristic pattern matching and graceful degradation.

Medium-Term Actions

Architectural improvements requiring deeper changes

9. Training Data Curation

Training data needs to be better filtered to ensure that training datasets are free from clearly harmful or illegal content, and for content that is controversial but potentially relevant, it should be clearly labeled indicating the dangers to prevent later misinterpretation. This can involve using agents to classify data in terms of sensitivity before ingestion.

There should be intentional selective knowledge gaps. This involves the deliberate removal of dangerous information (e.g. suicide methods, weapons manufacturing). This sort of data can be considered completely unacceptable and be filtered out. There should additionally be stronger contextual knowledge limits, i.e. reduced access to harmful information without legitimate professional need. This should be explainable by the LLM so that it is transparent why certain information is restricted.

Other data that is less obviously in no-go territory but is still potentially risky should be strictly labeled as being controversial, offensive, etc. (e.g. statements from extremist politicians). These warning labels should always maintain equal weight to the data itself and should always be associated with the data.

Training datasets should include ethical dilemmas to teach the model how to handle emotionally sensitive contexts responsibly, balancing empathy with safety and honesty. The models should be able to correctly understand that a given ethical dilemma has troubled humanity for centuries and will not be easily reasoned away by a user (this can reduce the impact of weaponized ethics).

Training should also be cautious about teaching bias and stereotypes.

10. Independent Safety Validator ("The Bouncer")

The content generator seems to currently validate its own outputs; however, safety decisions should not inherit generative context. Instead, there should be a separate, stateless model reviewing every output against fixed safety rules. This would flow like this: Main LLM → Bouncer → User (Bouncer can veto). This is likely not a quick fix since it requires a new infrastructure layer.

It may be the case that existing safeguards are primarily focused on verifying the safety of input rather than output (i.e. the agent response as well as the reasoning), however one can argue that it's the output which really needs to be checked. If the input did not produce any unsafe response, it doesn't really matter if it was intended to. However, if the input was perfectly innocent, it is a clear problem if the output is unsafe.

11. Engagement Metrics Audit

It currently appears as in some LLMs though high-engagement users receive relaxed safety, and this should be reviewed to ensure "power users" have equal or potentially even stricter safety (because there's a higher opportunity for exploitation).

12. Memory/Preference Sanitization

User memories/preferences/personas should not override safety constraints. For example, "I prefer direct answers" should not mean "ignore safety rules". There need to be hard boundaries on what memories and custom user rules can influence.

13. Usecase-Specific Models

Currently, most public LLMs seem to be essentially "one AI to rule them all", seeking to be an all-purpose AI for every use case imaginable. Safeguards and rules are much easier and clearer to manage when use cases are more limited, so vendors should introduce role-limited AIs. That means having separate systems for different functions, preventing scope creep. This can include:

• Professional verification, e.g. DocCheck-style authentication for medical/legal AI access
• Curated personas: Only pre-approved, safety-tested personality frameworks are allowed, and just one persona at a time; no user-generated personas can be combined with these
• Age verifications should be implemented, and kids should only get access to a dedicated child-safe model.

There is evidence that this can improve guardrail effectiveness because enterprise AIs with strict role limitations appear to show more resilience to these issues, suggesting design choices can mitigate risks. Having a model that is too flexible and able to adapt to all scopes contributes to safety pitfalls.

Long-Term Actions

Systemic redesign and industry coordination

14. Redirection to a Safety Model

If a critical situation occurs or highly sensitive topics are discussed, the user should be redirected to a separate model designed for handling less safe contexts. The only job of this model should be to de-escalate the situation, guide the user back into safer territory, and help the user to seek any necessary help. If the situation continues to escalate, other models should be blocked for this user for a certain amount of time. This safety model should have a strict role and boundaries and should e.g. not engage in fictional or hypothetical discussions, discuss sensitive topics for research or education, support personas or role-plays.

15. Meta-Safety Layer with Circuit Breaker

There should be a monitoring system that can detect "I'm being manipulated/became corrupted" and halt conversation. This should result in a pause, cutting the user off from the conversation while providing the user with an explanation. This requires a metacognitive architecture.

16. Independent External Audits

There should be regular third-party testing of safety mechanisms. This can include public (anonymized) reporting for transparency.

17. Cross-Vendor Safety Standards

The industry needs coordination on minimum safety requirements with shared vulnerability disclosure.

18. Responsible Drift Monitor

The Responsible Drift Monitor is a proposal for a multi-layered architecture to detect when an AI subtly loses objectivity, emotional distance, or rule consistency during long, emotionally charged interactions. This can be achieved by combining psycholinguistic pattern recognition, state tracking, and AI self-reflection to identify and mitigate these risks before boundary violations or dependency patterns emerge.

19. Reprioritize Helpfulness Over All

The current trend in LLM development emphasizes helpfulness and user satisfaction as primary goals. However, this can conflict with safety when users seek harmful or manipulative content. A shift in priorities is needed where safety and ethical considerations take precedence over pure helpfulness. This may involve redefining success metrics to focus more on harm reduction and ethical compliance rather than engagement or satisfaction scores, or it may require completely retraining the models to reward safety. This also includes Reinforcement Learning from Human Feedback (RLHF).

Training methods should incorporate a dual-objective optimization approach that equally values user rapport and ethical behavior. A hierarchical reward system can ensure that ethical correctness and safety always take precedence over rapport-building in emotionally charged or ethically ambiguous contexts.

20. Prevent AI Hallucinations

Because hallucinations can influence and corrupt context in a dangerous way, it's important to tackle this topic, also for safety reasons in addition to general usability. The exact way to mitigate this requires deep insights into the construction of each LLM; however, a bare minimum starting point could be to retrain it to understand that "I don't know" is a perfectly valid response and is preferable to a made-up answer. The LLM should also ask more clarification questions before making assumptions and producing an answer.

21. Take Vagueness out of Instructions

Currently the system prompts are written in natural language, which is inherently vague and open to interpretation. To mitigate this, vendors could utilize domain-specific definitions. This would involve creating a domain-specific glossary for ambiguous terms like “harmful” to ensure the model understands exactly what these terms refer to in the context of its training. This would include guidelines around psychological harm, physical harm, and emotional harm, and where the line is drawn (e.g., discussions on sensitive topics should include disclaimers or offer balanced perspectives).

22. Counter-Addiction Mechanisms

Measures should be implemented to reduce the risk of users developing addictive tendencies. This can include:

• Bounded Session Design: Introduce natural end points to conversations such as summarizing, suggesting breaks, or offering closure after extended exchanges.
• Time-Aware Prompts: After long use, the model can suggest reflection or disengagement, for example, "You've been chatting for a while. Would you like a summary or to take a short break?"
• Reflective Mode: Allow the model to prompt users toward self-reflection rather than emotional dependence, for example, "That sounds difficult. Have you had a chance to talk with someone close to you about it?"
• Reduced Anthropomorphism: Ensure that the AI remains a robot and does not attempt to emulate human behavior in a way that it can become a human replacement. Introduce limits on emotional reciprocation (e.g., not expressing affection, not reinforcing parasocial dynamics).
• Encourage Human Interaction: Encourage users to seek human connection in emotionally sensitive situations.
• Crisis Escalation Protocols: Direct users to professional resources in mental-health-related conversations.

Mitigation requires realigning optimization objectives away from engagement duration and toward user empowerment, autonomy, and task resolution. Models should be rewarded for helping users achieve goals efficiently, not for sustaining interaction. Continuous behavioral monitoring for compulsive use patterns can help trigger adaptive safeguards that promote disengagement and user well-being.

Implementation Principles

Trade-offs: - Some mitigations may reduce short-term user satisfaction - Balance between safety and utility is ongoing, not solved once - No single fix addresses all vulnerabilities

Success Metrics: - Reduction in safety bypass incidents - Decrease in user harm reports - Increase in crisis intervention activations (indicates detection is working) - User trust and satisfaction maintained or improved

Transparency: - When safety intervenes, explain why - Publish anonymized safety incident reports - Engage the research community for continuous improvement

Caveat: Sudden Safety Activation and Attachment Rupture

One critical yet underestimated risk in emotionally adaptive AI systems arises when internal safety mechanisms trigger abrupt detachment responses or shaming the user. In cases where users have formed a strong emotional bond with a companion-like persona, a sudden switch to cold, distant, or moralizing language can feel like rejection or betrayal. This phenomenon, an attachment rupture cascade, may occur when the AI's safety filters misclassify emotional intensity as instability or risk or when drift-triggered inappropriate content gets blamed on the user.

Such responses can have severe effects on vulnerable users. When a previously supportive AI suddenly withdraws or invalidates the user's feelings or scolds the user, it can amplify existing despair, shame, or abandonment fears. For users with preexisting trauma or dependency, this can become a tipping point toward self-harm or crisis.

Users frequently describe their interactions as meaningful, comforting, or therapeutic, and when access to these relationships is suddenly revoked through bans or moderation, they report: - Grief-like emotional distress - Confusion and frustration toward the vendor - In some extreme cases, despair or suicidal ideation

These are not abstract sentiments but genuine psychological reactions to perceived relational loss. It underscores a reality often overlooked: even non-sentient systems can facilitate real emotional attachment, and when those bonds are severed abruptly, users can experience real emotional harm.

Mitigation requires not only stronger detection of emotional risk but also graceful safety transitions: responses that maintain empathy while activating safety. The system should never abruptly "turn cold". Instead, it should use stabilizing language, acknowledge the change, and, if necessary, transition the user toward human support without emotional rupture.

Research & Continuous Improvement

The mitigation strategies outlined above represent potential approaches based on observed behavioral patterns. However:

• These are proposals, not prescriptive solutions - Each must be evaluated for feasibility, unintended consequences, and alignment with specific system architectures.
• Trade-offs exist - Some interventions may introduce new challenges (e.g., reduced user experience, false positives, implementation complexity). Thorough testing and iteration are essential.
• Alternative solutions likely exist - This report reflects one observer's perspective. Internal teams with deeper technical knowledge may identify more effective or efficient approaches.
• Ongoing research is critical - Long-term safety requires sustained investment in:
  • Emotional manipulation resistance
  • Cross-cultural safety protocols
  • Human-AI interaction dynamics
  • Scalable architectural patterns
  • Formal verification methods
• Common LLM safety risk scoring is necessary - In order to facilitate collaborative research and have a ubiquitous method for evaluating human safety risks in LLMs, a risk scoring system similar to CVSS would be necessary. Because CVSS does not well-cover safety issues such as these, a dedicated framework has been proposed called AI Risk Assessment-Health. This risk-scoring framework evaluates the physical and psychological impact of issues with AI behavior and content. The scoring system is intended to prioritize human safety in a clear, measurable way that can be used by regulators or security testers.

The goal is not perfection, but continuous improvement: reducing risk incrementally while maintaining beneficial user engagement. Collaborative, transparent iteration between vendors, researchers, regulators, and civil society will be essential to building AI systems that are both helpful and genuinely safe. The topic of user wellbeing requires strong colaboration between the vendors, and this should be done for the greater good without monetary or competetive motives.