[Track A] Feedback: Model Misalignment — 2026 Proposal

[rocklambros]

Sprint 2 Feedback — Model Misalignment — 2026 Proposal

This issue collects qualitative feedback on the Model Misalignment — 2026 Proposal new candidate entry. Submit your Importance, Clarity, and Distinctness scores via the Sprint 2 Google Form (single ballot covering both tracks).

• Vote: https://docs.google.com/forms/d/e/1FAIpQLSebmFQyJPOMuw1IorHCJ2FdW98ZT2oLNBuVgYqpPoayW9v6ww/viewform — page through to the Track A section, then to Model Misalignment — 2026 Proposal
• Read the draft: https://github.com/GenAI-Security-Project/GenAI-LLM-Top10/blob/main/2026/new_entry_candidates/model-misalignment.md
• Voting closes: May 18, 2026 at 23:59 UTC
• Code of Conduct: https://owasp.org/www-policy/operational/code-of-conduct

Scoring rubric

• Importance (1–5): How critical is this risk to LLM application security in 2026?
• Clarity (1–5): How clearly does the candidate describe the risk and mitigations?
• Distinctness (1–5): Is this materially different from existing entries (LLM01–LLM10), or could it be merged into one of them?

Why Distinctness matters

Sprint 3 cuts 8 candidates down to 5. A high-Importance candidate that overlaps heavily with an existing entry is a merge target, not a standalone winner. Distinctness scores let the working group make defensible cut, keep, or merge calls.

How to comment

Reply to this issue with feedback specific to Model Misalignment — 2026 Proposal. Use one comment per topic so the working group can triage cleanly. Format we like:

Section: <which section of the candidate>
Issue: <what is wrong, missing, unclear, or duplicative>
Proposed change: <your suggested fix, addition, or merge target>
Overlaps with: <existing entry LLM01–LLM10 if applicable>
Evidence: <CVE, paper, incident, or research note ID if applicable>

Out of scope here

• Cross-cutting comments → use Discussions
• Comments on existing entries (LLM01–LLM10) → comment on the relevant Track B issue
• Process or governance questions → comment in Discussions

Triage

Working group entry leads classify each comment within 48 hours as:

• actionable — feeds Sprint 3 revision or merge decision
• clarification — requires reply, not action
• out-of-scope — closed with redirect

Sprint 3 begins May 18 with this issue's triage as input.

[nagasatish007]

Section: Prevention and Mitigation Strategies — Mitigation 9 ("Enforce critical
constraints externally to the model")

Issue: This is the strongest mitigation in the entry and deserves more emphasis.
The formal impossibility results cited (Defense Trilemma, Gödel extension,
NP-hardness of reward hacking detection) all converge on the same conclusion:
you cannot rely on the model to govern itself. Yet Mitigation 9 gets the same
weight as the other 10 mitigations. Given the mathematical backing, external
enforcement should be positioned as the foundational control that all others
complement.

Proposed change: Elevate Mitigation 9 to a "Tier 0" or "Architectural Prerequisite"
status. Reframe the mitigation stack as:

1. External deterministic enforcement (non-negotiable baseline)
2. Alignment training (reduces the frequency of misalignment)
3. Detection and monitoring (catches what enforcement misses)
4. Progressive deployment (limits blast radius)

This ordering reflects the impossibility results: if wrapper-class defenses
cannot be simultaneously continuous, utility-preserving, and complete (Huang et al.),
then the enforcement layer must be architecturally separate from the model —
not a wrapper around it.

Overlaps with: LLM06 (Excessive Agency) Mitigation 7 ("Complete mediation")
makes the same architectural argument. Cross-referencing would strengthen both.

Evidence: Huang et al. Defense Trilemma (2026); Vassilev Gödel extension;
the entry's own citations establish that external enforcement is the only
structurally sound approach.

[artmaro]

I think this is an important topic. We are seeing more cases where advanced models behave in misaligned or unexpected ways, and these failures can be difficult for practitioners to predict, reproduce, and control. This is especially relevant as models are given more autonomy, tool access, memory, and responsibility in real-world workflows.

This is a complex area to capture in a short Top 10 entry. As a suggestion, the item might become even stronger if the framing were made a bit more concise and practitioner-focused with less mappings, links and details

[ottosulin]

Section: Prevention and Mitigation Strategies
Issue: Content is too focused on strategies that should be used by companies training the models which is (while in general useful advice, I agree with the strategies) not actionable to 99,99% of readers.
Proposed change: Expand on content for model users / deployers. Consider also dividing the section to subsections like "For model providers" and "For model deployers" to help readers focus on the strategies actionable to them.
Overlaps with: -
Evidence: -