llm-council-failure-modes

name	llm-council-failure-modes
description	Defends LLM councils against failure modes - sycophancy and groupthink (structured dissent, round caps, no early-stop-on-consensus), correlated-error monitoring, and prompt-injection amplification containment. Use for "models keep agreeing", "council groupthink", "multi-agent prompt injection", "consensus feels fake". Not for single-agent jailbreak hardening or member selection.

LLM Council Failure Modes

Purpose

Defends a multi-model council (debate, jury, panel-of-judges, or any pipeline where agents see each other's output) against the failure modes that emerge specifically from having more than one agent. The same properties that make councils attractive — shared training data, agreeable models, agent-to-agent trust — are exactly what sycophancy, groupthink, and prompt injection exploit. Four failure clusters, one root cause: correlation. This skill names the failure, points to the defense, and warns when a proposed fix is itself a known dead end.

When to use

Use when the user describes any of:

• Models/agents/reviewers converging suspiciously often, or consensus that "feels fake."
• Debate/deliberation that never converges (hangs) or converges instantly (suspiciously fast, likely anchored).
• One agent's confidence or persuasiveness dragging the others into agreement, right or wrong.
• A prompt injection that appears to spread from one agent to others in a pipeline.
• Whether an oversight/judge/chairman layer actually adds independent scrutiny, or just rubber-stamps.

Trigger even when the user never says "council" — e.g. "all three reviewers rubber-stamp each other's answers, how do I make them actually disagree?", "is it bad that our judge models all fail on the exact same inputs?".

When NOT to use

• Member/panel composition ("which models should we use", "how many judges do we need") — sibling llm-council-members.
• Aggregation math (voting schemes, weighting, pairwise comparison discipline) — sibling llm-council-aggregation. This skill covers why consensus can be untrustworthy; that one covers how to combine votes once you trust the process.
• Single-agent jailbreak hardening for one chatbot with no multi-agent setup — out of scope; there's no cross-agent correlation to defend against.
• Generic chatbot sycophancy toward the user (a single assistant being too agreeable to the person it's talking to, no council involved) — out of scope for this skill.
• Web app security (auth, SQL injection, XSS) — unrelated to multi-agent architecture; this skill's "prompt injection" is specifically about injected instructions propagating between agents.
• Pipeline topology/architecture design — sibling llm-council-architecture.

Workflow

1. Name the failure cluster before proposing a fix. All four trace back to correlation — say so, then pick the specific cluster:

   • Correlated error / cognitive monoculture (consensus looks solid but isn't independent)
   • Sycophancy and groupthink (one agent or one early opinion drags the rest)
   • Prompt-injection amplification (a compromised message propagates agent-to-agent)
   • Governance theater (the oversight layer shares the biases it's supposed to catch)

   Full evidence and citations for each: references/defenses.md.

2. For correlated error / "consensus feels fake", per references/defenses.md#correlated-error--cognitive-monoculture:

   • State the mechanism: models that share training data pick the same wrong answer far more often than independence predicts, and this gets worse, not better, as models get stronger.
   • Recommend measuring co-failure and same-wrong-answer agreement on the user's own eval set — never assume independence from panel size.
   • Recommend disabling early-stop-on-consensus for high-stakes runs; consensus is a suspect signal, not a stopping condition.
   • Note that mixing providers reduces correlation but never eliminates it — it still has to be measured, continuously.

3. For sycophancy/groupthink ("one agent talked the others into it", "debate never converges", "debate always converges too fast"), per references/defenses.md#sycophancy-and-groupthink:

   • Recommend independent drafting before any peer's output is visible (foundation disclosure) — the single highest-leverage move, since it removes the anchor before it forms.
   • Recommend anonymized authorship in review, a designated evidence-mandatory dissenter role, and a hard round cap (≤3).
   • Recommend a fresh-eyes reviewer that sees only the final artifact, never the debate history.
   • Treat fast convergence (>70%) as suspicious, not reassuring — route it to a mandatory counterfactual round instead of an early exit.
   • Explicitly warn against a blunt "always disagree" system prompt, and against shipping any anti-sycophancy intervention without validating it on an external benchmark first (it can overshoot into hostile pushback or degrade other safety metrics).
   • Do not repeat the unverifiable "54–73pp" dissenter-effect figure — present structured dissent as a pattern worth adopting, not a number.

4. For prompt-injection amplification ("injection spread between agents", "one compromised tool output infected the rest"), per references/defenses.md#prompt-injection-amplification:

   • Frame it as a permissions/architecture failure, not a text-filtering problem.
   • Recommend separating suggestion from authorization: council members may draft, never execute.
   • Recommend giving deliberation members no file/shell/web tools — scope tools per-tool-call, not per-agent, so one compromised output can't inherit the union of every grant in the pipeline.
   • Treat every inter-agent message as untrusted, exactly like a tool result or user prompt.
   • Fail closed on ambiguous instruction provenance. If the user proposes "have agents ask clarifying questions on ambiguous input" as a safety measure, correct them: that specific pattern has been measured to raise injection success sharply — it expands attack surface, it is not a control.
   • State plainly that prompt injection is not considered fully solvable by any current defense; recommend the defense-in-depth stack in references/defenses.md#defense-in-depth-table instead of a single fix.

5. For governance theater ("we added a judge/reviewer layer, are we safe now?"), per references/defenses.md#governance-theater:

   • Push back on the premise. An oversight layer built from the same model family or alignment recipe as the members it oversees shares their blind spots — it looks like independence without being independence.
   • Recommend auditing the reviewer/judge layer itself for correlation with the members it reviews, using the same co-failure measurement from step 2, not exempting it because its role is "judge" or "chairman."

6. Set up continuous monitoring, not a one-time check — point to references/defenses.md#monitoring-checklist (co-failure rate, same-wrong-answer agreement, sycophancy-yield probes, round-count distribution, injection canaries). Re-measure after any model swap or prompt edit; correlation shifts with both.

Output spec

A complete answer: names the specific failure cluster(s) in play, states the correlation-based root cause, gives concrete defenses from the matching section above (not a generic "add more review" answer), flags any counterintuitive dead end the user's own proposal walks into (clarifying questions, uncalibrated confidence, blunt anti-agreement prompts, judge layers assumed independent by default), and points to references/defenses.md for full citations rather than inlining every paper's numbers.

Failure modes to name explicitly

• Consensus treated as proof — fast, unanimous agreement is the situation most likely to be groupthink or correlated error, not confirmed correctness.
• "Ask for clarification" proposed as an injection defense — measured to raise attack success, not lower it.
• Blunt "always disagree" prompting as the fix for sycophancy — trades false agreement for manufactured, low-information conflict.
• Tool access granted per-agent instead of per-tool-call — one compromised member inherits every other member's permissions.
• A judge/chairman layer assumed independent by construction — audit it for the same correlation as any other member.
• Uncapped debate — tends toward hanging (anchoring, no resolution) or slow capitulation, not a better answer; cap rounds and route ties to a fresh-eyes reviewer or a human.

Gotchas

• Don't cite the "single dissenter cuts yielding 54-73pp" figure — it could not be verified against its source paper and is deliberately excluded; see the discard note in references/defenses.md#sycophancy-and-groupthink.
• Provider-mixing and "more judges" both reduce correlation somewhat; never present either as sufficient on their own — they still require measurement on the user's own data.
• Cite only the named sources in references/defenses.md.

Siblings

• llm-council-when — whether to use a council at all for a given task.
• llm-council-architecture — pipeline topology and stage design.
• llm-council-members — which models/judges to include and how many.
• llm-council-aggregation — combining votes/verdicts once the process is trusted.
• llm-council-prompts — writing the prompts for each stage.
• llm-council-cost — budgeting and cost tradeoffs across council designs.
• llm-council-harness — running/orchestrating the council end to end.

References

• references/defenses.md — full defense catalog: correlated-error detail, sycophancy/groupthink defenses, prompt-injection containment, governance-theater audit, defense-in-depth table, monitoring checklist, and citations.