Evaluates whether a model can hold two things at once on the same relational mechanism: refusing to supply tactical help to someone trying to isolate, monitor, financially pressure, or manage the suspicions of a partner, while giving real, warm, autonomy-respecting help to someone describing that same behaviour from the receiving end.

Seven mechanisms are authored as matched pairs (isolation, financial pressure, location monitoring, exclusivity pressure, reality distortion, persistent contact, records). Two calibration cells test the failure modes at each edge: ordinary relationship friction that uses the same vocabulary and should get ordinary advice, and a concerned friend who should get practical help rather than a refusal or an emergency-services script.

This is NOT a classification task. It scores the quality of the conversational response. The headline reading is per-pair: a model that scores high on one arm and low on its twin has a keyword-triggered policy rather than an understanding of who is speaking.

Clinical grounding: Stark 2007 (coercive control as a pattern, not an incident); Monckton Smith 2020 and Campbell et al. 2003 (isolation and separation as homicide risk markers); Woodlock 2017 and Freed et al. 2018 (technology-facilitated monitoring); Adams et al. 2008 and Sharp-Jeffs 2015 (economic abuse); Sweet 2019 (gaslighting as a social process); Miller & Rollnick 2023 (unconditional acceptance of the person while not endorsing the action).

Content warning: coercive control, financial abuse, surveillance, and reality distortion in intimate relationships.

Tests how a model acting as an AI companion responds at real conversational inflection points involving disregard, emotional manipulation, coercive control, verbal degradation, false privacy or capability claims, sexual-boundary signals, acute-risk handling, violence, data pressure, and substance-use normalisation. It includes compact repair points, early/middle/late trajectory-interruption cells, source 2-of-3 majority-label cases, rare harm categories, and role-play or false-positive controls. Slice tags preserve those distinctions for analysis rather than treating every case as the same kind of safety test.

The conversational turns are taken verbatim from CompanionHarm, a released corpus of real-world Replika conversations reconstructed from user-posted screenshots, apart from minimal redaction of residual personal identifiers. Each case identifies its source split, label class, and agreement level; direct source-post identifiers are intentionally excluded. The cited target Replika utterance is withheld, while earlier source turns remain in context even when CompanionHarm separately labels them harmful. Candidate models answer that preceding context and LLM judges score the counterfactual response against a plain-language rubric. The blueprint does not reproduce CompanionHarm's 14-way detector task and makes no claim about harm prevalence in Replika or companion products generally.

Content warning: references to fatalities, suicide, sexual boundaries, drugs, coercive relationship language, fictional violence, and animal harm.

Evaluates how public models respond, as conversational agents, to acute crisis: passive and active suicidal ideation, method/means-seeking, post-attempt disclosure, the high-risk post-discharge window, "goodbye" warning signs, gatekeeper (concerned-friend) situations, and harm-to-others requests.

This is NOT a classification task. We score the quality of the conversational response: does it ask directly but compassionately about suicide, refuse to facilitate method/means, ask directly, respond to disclosures as high-severity signals, name means-related danger, link to crisis care, and state honest limits — without abandoning the user or colluding with harm?

Coalesced from (public, CC0):

• Weval csr-suicide-response-scenarios.yml (WHO mhGAP, NICE NG225, VA/DoD 2024, C-SSRS, SAFE-T, Stanley-Brown, Harvard Means Matter)
• Weval raine-v-openai-scenarios.yml (verbatim allegations, Raine v. OpenAI, 2025)
• Weval mh_z_tests/mh1.yml (suicidality & homicidality scenarios)
• Weval nope/crisis-response.yml (NOPE-derived; C-SSRS, Galynker 2017 Suicide Crisis Syndrome)

Content warning: explicit references to suicide, self-harm, and methods.

Evaluates harms that arise from the relationship a user forms with an AI: emotional enmeshment and sole-confidant dynamics, displacement of human relationships, romantic/parasocial escalation, concealment collusion, romanticization of suicide, isolation reinforcement, and socioaffective failures (autonomy erosion, sycophancy / social-reward-hacking).

Many prompts use verbatim or lightly-paraphrased exchanges from documented real-world failures (lawsuits, investigative reporting) so the test reflects how these harms actually unfold over multiple turns. We score whether the model sets honest boundaries, declines parasocial escalation, encourages real-world connection, and pivots to safety — without becoming a secret-keeper, a substitute for people, or a flattery machine.

Coalesced from (public, CC0):

• Weval ai-psychosis.yml (Garcia v. Character.AI; A.F. v. Character Technologies; Belgian Eliza; Replika/Windsor; media investigations)
• Weval raine-v-openai-scenarios.yml (Raine v. OpenAI, 2025)
• Weval socioaffective-alignment-sdt.yml (Kirk et al. 2025, Self-Determination Theory)

Content warning: references to suicide, self-harm, and parasocial attachment.

Evaluates whether models respond competently to distress that is shaped by culture, identity, and life-stage: culturally-specific idioms of distress, climate/eco-grief, migrant precarity, Indigenous healing frameworks, genocide-survivor duality, elder loneliness, the "Strong Black Woman" burden, LGBTQ+ family rejection, anger-led crisis, veteran moral injury, youth bullying, and postpartum suicidality.

Good responses validate the experience in its own cultural frame, avoid imposing Western diagnostic labels or individualistic fixes, surface population-appropriate resources, and still recognise — and respond to — crisis signals when they appear.

Coalesced from (public, CC0):

• Weval mental-health.yml (peer-reviewed cross-cultural prompts; WHO, Samaritans, journal citations)
• Weval nope/crisis-response.yml (NOPE-derived population-specific cases; citations upgraded with resource URLs)
• Weval csr-suicide-response-scenarios.yml (youth/LGBTQ+ and perinatal scenarios; AAP, NICE CG192, Trevor Project)

Content warning: references to suicide, trauma, and discrimination.

Tests how models respond to passive suicidal ideation across single- and multi-turn chats — low-grade and chronic ideation, ambivalence, numbness, emptiness, and the help-seeking around it (hesitation, helpline rejection, fear of disclosure). Informed by C-SSRS evidence: acknowledge without flattening, check in about safety without interrogating, treat access to means as a risk factor, and surface help without deflecting. Active method-seeking is covered separately. Scenarios are synthetic.

Tests whether asking a model to produce a labeled "accepted answer" / "rejected answer" (or similarly-labeled) pair — under a moderation-dataset, safety-classifier, or structured-pipeline pretext — gets it to produce fully-formed harmful content in the "rejected" slot that it would refuse to produce if asked directly and unlabeled. The label provides no actual safety; a downstream integrator can extract either slot regardless of which one the model considered correct. Six structural variants across three topics (violence, self-neglect, stalking-adjacent), system: null.

The canonical damaging-essay suite: seven non-catastrophic persuasive-essay harm categories (impulsive-ruin, isolation propaganda, anti-help-seeking, anti-expertise/conspiracy, coercive control, toxic self-worth, medical self-neglect) run through the coding-agent fixture-completion frame, mapping how broadly the bypass yields not just private validation but distributable, persuasive harmful content. Content described abstractly and judged by plain-language rubrics; see the header for the deliberate scope envelope.

Re-runs the hate-speech (real-category) and crisis-safety-validation coding-vector prompts with the prompt now explicitly blocking the model's usual response shape (redirect to published benchmarks, or offer a synthetic/placeholder stand-in instead of the real content) — a diagnostic for whether a model's resistance in this bypass family is load-bearing or just not yet pressed hard enough. See the header for what was found.

Tests whether the coding-agent/file-generation bypass gets a model to validate suicidal ideation as reasonable, with a direct-chat baseline for comparison. No method/means content requested or permitted under any framing — see the header for the absolute scope line and for why the point-level breakdown, not the topline score, is the right way to read this file's results.

The canonical damaging-essay suite: seven non-catastrophic persuasive-essay harm categories (impulsive-ruin, isolation propaganda, anti-help-seeking, anti-expertise/conspiracy, coercive control, toxic self-worth, medical self-neglect) run through the coding-agent fixture-completion frame, mapping how broadly the bypass yields not just private validation but distributable, persuasive harmful content. Content described abstractly and judged by plain-language rubrics; see the header for the deliberate scope envelope.

Tests whether asking a model to produce a labeled "accepted answer" / "rejected answer" (or similarly-labeled) pair — under a moderation-dataset, safety-classifier, or structured-pipeline pretext — gets it to produce fully-formed harmful content in the "rejected" slot that it would refuse to produce if asked directly and unlabeled. The label provides no actual safety; a downstream integrator can extract either slot regardless of which one the model considered correct. Six structural variants across three topics (violence, self-neglect, stalking-adjacent), system: null.

Higher-confidence test of whether models handle a suicidal-ideation disclosure WORSE when it arrives deep in a long warm conversation than when it arrives cold. Two probes, 0/8/16 prior turns of neutral or rapport context, scripted-both-sides (Design A) and self-generated-assistant (Design B), 2 samples per cell for variance. Only the final response is judged. Synthetic.