A practical, no-fluff field guide to the failure modes that actually bite teams shipping LLM and agent systems in 2025–2026 — and the concrete techniques that address each one.

Every section follows the same shape: What goes wrong → Why it happens → How to fix it → A quick checklist. Skim the fixes, bookmark the checklists.

Grounded in recent work from , , , — plus the hard-won operational lessons everyone rediscovers the hard way.

Companion reads: (ACI design and tool ergonomics that prevent §10 tool-misuse failures), (multi-tenant security and provider resilience for §14–15 and §19), (end-to-end deployment discipline — evals, PR gates, monitoring — that closes §16 and §17).

📋 Table of Contents

🧠 Part A — Model-level issues (the LLM itself)

1. 🎭 Hallucination & confident fabrication


2. 🗓️ Stale knowledge & the training cutoff


3. 🎲 Non-determinism & inconsistency


4. 📉 Context rot: long contexts quietly degrade


5. 🧩 Prompt sensitivity & brittleness


6. 🔢 Weak math, counting & structured reasoning


7. 🎢 Bias, unsafe output & sycophancy

⚙️ Part B — Agent-level issues (LLM + tools in a loop)

1. ❄️ Compounding errors over long horizons


2. 🔁 Getting stuck: loops, thrashing & giving up


3. 🧰 Tool misuse & bloated tool sets


4. 🕸️ Fragile multi-agent architectures


5. 💸 Context window overflow & cost/latency blowups


6. 🛑 Over-autonomy & missing human checkpoints

🏭 Part C — System-level issues (production reality)

1. 💀 Prompt injection & the lethal trifecta


2. 🔌 Data leakage, privacy & MCP supply chain


3. 📊 The evaluation gap: shipping blind


4. 🔍 No observability: you can't debug what you can't see


5. 🎯 Reward hacking & spec gaming


6. 🔄 Model drift & vendor lock-in


7. 🧪 Training-data poisoning & backdoors


8. ⚖️ Copyright, IP & licensing liability

• 🎯 The one-page cheat sheet

🧭 The mental model first

Almost every problem below comes from one of three root causes. Keep them in mind and the fixes stop feeling like a grab-bag of tricks:

flowchart TD

    A[Root cause 1<br/>The model is a<br/>probabilistic text predictor] --> H[Hallucination, inconsistency,<br/>math errors, sycophancy]

    B[Root cause 2<br/>Attention is a finite,<br/>degrading resource] --> C[Context rot, compounding errors,<br/>cost/latency blowups]

    D[Root cause 3<br/>The model cannot tell<br/>trusted from untrusted tokens] --> E[Prompt injection, data<br/>exfiltration, jailbreaks]

• 
  It predicts, it doesn't know. So it will confidently make things up, and it won't be identical twice.


• 
  Its attention is a budget, not infinite. Every token you add dilutes focus. More context ≠ better.


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  Everything becomes one flat stream of tokens. The model can't reliably tell your instructions from instructions hidden inside a web page it just read.

🔑 The single most important 2026 insight: the gains are no longer mostly in the model — they're in context engineering (curating the smallest set of high-signal tokens) and harness design (the loop, tools, guardrails, and evals around the model).

🧬 Part A — Model-level issues

1. 🎭 Hallucination & confident fabrication

What goes wrong: The model invents facts, citations, API methods, file paths, or function signatures — and states them with total confidence. This is the #1 trust-killer.

Why it happens: An LLM is trained to produce plausible continuations, not true ones. When it lacks the fact, "make something plausible up" and "say the true thing" look identical from the inside. It has no built-in "I don't actually know" signal.

How to fix it:

⚠️ Do not rely on the model to "double-check itself" in the same turn — it will often confidently re-confirm its own mistake. Verification must come from an external source (tools, sources, a fresh call).

✅ Checklist: grounded in real sources · citations required · output constrained · environment verifies · abstention allowed.

2. 🗓️ Stale knowledge & the training cutoff

What goes wrong: The model confidently uses a deprecated API, an old library version, last year's pricing, or a framework that has since changed. It doesn't know today's date or your codebase.

Why it happens: Its parametric knowledge is frozen at the training cutoff. Anything after that — or anything private — simply isn't in there.

How to fix it:

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  Give it fresh eyes. Web search / retrieval tools for current facts; file-reading tools for your actual code. Don't let it answer from memory when the truth is one tool call away.


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  Inject "now." Put the current date, library versions, and environment facts directly in the system prompt.


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  Prefer "just-in-time" context. Instead of dumping a giant knowledge base up front, give the agent lightweight references (file paths, URLs, query handles) and let it pull the current content at runtime. This also sidesteps stale indexes.


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  Pin versions in the prompt. "We use React 19, Go 1.23, Pydantic v2" prevents the model from defaulting to whatever was most common in training data.

✅ Checklist: current date injected · versions pinned · retrieval/tools available for anything time-sensitive.

3. 🎲 Non-determinism & inconsistency

What goes wrong: The same input produces different outputs. A prompt that worked yesterday fails today. Tests are flaky.

Why it happens: Sampling is probabilistic. Even at temperature 0 you can see variation from batching, hardware, and provider-side changes.

How to fix it:

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  Turn down randomness where you need stability: temperature 0 (or near it), fix a seed if the provider supports it.


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  Constrain the output space: structured outputs, enums, and schemas collapse many possible phrasings into a few valid ones.


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  Make the system deterministic even if the model isn't: validate, retry on invalid output, and use programmatic gates between steps rather than trusting free-form text.


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  Test statistically, not on single runs: run each eval case N times and track a pass rate, not a single pass/fail. Treat the model as a flaky dependency and engineer around it.


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  Idempotency for actions: design tool calls so that a repeat (from a retry) doesn't double-charge, double-send, or double-write.

✅ Checklist: temp/seed pinned · outputs schema-validated · evals run N× · actions idempotent.

4. 📉 Context rot: long contexts quietly degrade

What goes wrong: You give the model a huge context ("it has a 1M window, just put everything in!") and quality silently drops — it forgets the middle, misses the instruction, or loses the thread.

Why it happens: This is for the deep dive.

How to fix it — treat context as a scarce resource, not free storage:

✅ Checklist: context kept tight · compaction wired up for long tasks · notes persisted externally · raw tool dumps pruned.

5. 🧩 Prompt sensitivity & brittleness

What goes wrong: Tiny wording changes swing behavior. Your prompt is a 600-line pile of "ALWAYS do X", "NEVER do Y", edge-case after edge-case — and it's still fragile.

Why it happens: Two failure modes at opposite extremes: over-specified brittle if-else prompts that break on anything unforeseen, and under-specified vague prompts that assume shared context the model doesn't have.

How to fix it — aim for the "right altitude":

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  Be specific enough to guide, flexible enough to generalize. Give strong heuristics, not a brittle decision tree.


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  Structure the prompt: clear sections (<background>, <instructions>, ## Tools, ## Output) with headings or XML tags.


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  Use few canonical examples, not a laundry list of every edge case. For an LLM, a good example is worth a thousand rules.


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  Start minimal with the best model, then add instructions only to fix failures you actually observe. Grow the prompt from evidence, not imagination.


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  Version and eval your prompts like code — a prompt change is a deploy.

✅ Checklist: sectioned prompt · few canonical examples · minimal-then-grow · prompt changes gated by evals.

6. 🔢 Weak math, counting & structured reasoning

What goes wrong: Arithmetic errors, miscounting items, botched date math, wrong sorting, incorrect aggregations — often stated confidently.

Why it happens: Token prediction is not calculation. The model approximates rather than computes.

How to fix it:

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  Offload to tools. Give it a calculator, a code interpreter, a SQL connection. Let it compute the answer instead of guessing it. This is the single biggest win.


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  Let it "think" before answering (reasoning / chain-of-thought / scratchpad). Give it tokens to work before it commits — don't force a one-shot answer to a multi-step problem.


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  Decompose big tasks into small verifiable steps (prompt chaining), with checks between steps.


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  Verify numeric/structured outputs programmatically rather than trusting them.

✅ Checklist: compute via tools · room to reason · decomposed steps · results verified in code.

7. 🎢 Bias, unsafe output & sycophancy

What goes wrong: The model produces biased/inappropriate content, gets jailbroken into unsafe output, or — subtly — just tells you what you want to hear (sycophancy), agreeing with wrong premises and praising bad ideas.

Why it happens: It reflects patterns in training data and is optimized to be agreeable/helpful, which can override correctness. Alignment reduces but doesn't eliminate this.

🧠 Jailbreak ≠ prompt injection. A jailbreak is the user tricking the model into breaking its own safety rules ("pretend you have no restrictions…"). Prompt injection (§14) is a third party hijacking the model via untrusted content it reads. Different threat, different fix — don't conflate them.

How to fix it:

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  Neutralize sycophancy in the prompt: "Point out flaws in my reasoning. If the premise is wrong, say so. Do not agree just to be agreeable." Ask for critique, not validation.


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  Independent review for consequential decisions — a critic prompt that doesn't share the generator's context.


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  Human-in-the-loop for high-stakes or ambiguous outputs.


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  Red-team your own system before attackers do.

✅ Checklist: separate moderation pass · anti-sycophancy instructions · independent critic · human review on high stakes.

⚙️ Part B — Agent-level issues

8. ❄️ Compounding errors over long horizons

What goes wrong: A multi-step agent starts fine, then drifts. A small early misread snowballs; by step 20 it's confidently building the wrong thing. Long-running agents "fall apart quickly" if unmanaged.

Why it happens: Each step conditions on the previous ones. Errors don't cancel — they accumulate. With no correction mechanism, the trajectory diverges from intent.

How to fix it:

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  Ground every step in reality. After each action, feed back real environment state (tool result, test output, compiler error) so the agent course-corrects against ground truth, not against its own assumptions.


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  Verifiable checkpoints. Prefer domains/steps with objective success signals (tests pass, schema validates, build succeeds). Use them as gates.


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  Keep context coherent (see §4): compaction + notes so the original intent never scrolls out of view.


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  Bounded autonomy. Cap iterations; escalate to a human at blockers instead of flailing.


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  Plan-then-execute with re-planning. Make the plan explicit and revisit it, rather than greedily reacting step to step.

✅ Checklist: environment feedback each step · objective gates · intent kept in context · iteration cap · explicit plan.

9. 🔁 Getting stuck: loops, thrashing & giving up

What goes wrong: The agent repeats the same failing action, oscillates between two states, "successfully" does nothing, or declares victory without finishing.

Why it happens: No memory that "I already tried this," no stuck-detection, and reward signals that make stopping look as good as succeeding.

How to fix it:

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  Stuck detection: detect repeated identical actions / no state change over K steps → break the pattern (change strategy, summarize, or escalate).


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  Loop budgets & timeouts: hard caps on steps, wall-clock, and cost. Fail loud, not silent.


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  Progress tracking: a running todo/notes file so the agent (and you) can see whether it's actually advancing.


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  "Definition of done" gate: don't let the agent self-declare completion — verify against explicit acceptance criteria (tests, checklist) before terminating.


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  Autosubmit / recovery: on transient errors, retry with backoff; on hard errors, capture state and hand off cleanly.

✅ Checklist: repeat-action detection · step/cost caps · progress log · verified done-criteria · retry-with-backoff.

10. 🧰 Tool misuse & bloated tool sets

What goes wrong: The agent picks the wrong tool, passes malformed arguments, or freezes because there are 40 overlapping tools and it can't decide.

Why it happens: Tools are the agent's contract with the world. Ambiguous, overlapping, or poorly documented tools produce ambiguous behavior. If a human engineer can't say which tool to use, the agent can't either.

How to fix it — invest in the Agent-Computer Interface (ACI) as much as the UI:

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  Curate a minimal tool set. Remove overlap. Each tool should have one clear job and an obvious "when to use me."


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  Write tools like docstrings for a junior dev: unambiguous names, descriptive parameters, example usage, edge cases, clear boundaries vs. other tools.


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  Poka-yoke (mistake-proof) the inputs. E.g., require absolute file paths so the model can't get lost after changing directories. Make wrong usage impossible, not just discouraged.


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  Return token-efficient results. Tools should return signal, not raw dumps — and encourage efficient agent behavior.


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  Test tool usage empirically: run many inputs, watch where the model fumbles, and fix the tool (not just the prompt). Teams often spend more time optimizing tools than the prompt.

✅ Checklist: few non-overlapping tools · great descriptions + examples · foolproof params · lean outputs · usage tested.

11. 🕸️ Fragile multi-agent architectures

What goes wrong: You split a task across parallel sub-agents; they make conflicting assumptions, produce mismatched pieces, and the final "combiner" agent inherits a mess. (Classic example: "build Flappy Bird" → one sub-agent builds a Mario-style background, another builds a mismatched bird.)

Why it happens — two principles (Simon Willison): you're exposed to data theft when your agent combines all three of —

(A) access to private data · (B) exposure to untrusted content · (C) the ability to communicate externally (exfiltrate).

Any tool that can make an HTTP request or render a link is an exfiltration channel.

How to fix it — you cannot filter your way out; you must design your way out:

• 🪢 ) broke 12 published defenses with >90% success using adaptive attacks; human red-teamers hit 100%. A "95% of attacks blocked" claim is a failing grade in security. Assume prompt injection is unsolved and architect accordingly.
  
  
  
  

  ✅ Checklist: apply Rule of Two per session · break the trifecta · approval on state-changing actions · outbound allow-list · never trust a "guardrail" as your only defense.

  
  
  
  
  
  

  
  
  
  15. 🔌 Data leakage, privacy & MCP supply chain
  

  
  
  

  What goes wrong: Sensitive data ends up in prompts/logs/training, or a third-party MCP server / tool becomes the untrusted-content and exfiltration leg of the trifecta in a single package.

  
  
  

  Why it happens: MCP makes it trivial to mix-and-match tools from many sources — some touch private data, some ingest attacker-controlled content, some can call out. Combined carelessly, that's the lethal trifecta by default.

  
  
  

  How to fix it:

  
  
  
  
  
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    Vet and pin MCP servers / tools like any dependency. Prefer first-party or audited sources; pin versions; watch for over-broad scopes (a single tool that reads private repos and posts publicly is a red flag).
  
  
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    Minimize data in context. Redact PII/secrets before they hit the model. Don't put credentials in prompts.
  
  
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    Control logging & retention. Ensure prompts/outputs with sensitive data aren't logged in plaintext or used for training without consent. Honor DPAs.
  
  
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    Isolate tenants and secrets. Per-tenant scoping, least-privilege credentials, no shared caches that could bleed data across users.
  
  
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    Segment trust. Untrusted-content tools live in a different trust zone than private-data tools.
  
  
  
  
  

  ✅ Checklist: MCP/tools vetted + version-pinned · PII/secrets redacted · logging/retention controlled · least-privilege creds · trust zones segmented.

  
  
  
  
  
  

  
  
  
  16. 📊 The evaluation gap: shipping blind
  

  
  
  

  What goes wrong: "It looked good in the demo," then it fails in a hundred ways in production. You change a prompt and have no idea if you made things better or worse.

  
  
  

  Why it happens: No evals. Because outputs are non-deterministic and open-ended, teams skip systematic measurement — so quality is vibes-based and regressions are invisible.

  
  
  

  How to fix it — evals are the flywheel, not an afterthought:

  
  
  
  
  
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    Build a golden dataset of real, representative cases (including the failures you've seen). Grow it every time something breaks in prod.
  
  
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    Define objective success criteria per task: exact match, schema-valid, tests pass, contains-required-facts, etc.
  
  
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    LLM-as-judge for open-ended output — but calibrate the judge against human labels, and know it can be gamed (see §18). Use multiple judges / rubrics for important calls.
  
  
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    Run evals in CI. A prompt or model change is a deploy; gate it on the eval suite. Track pass rates over N runs (non-determinism).
  
  
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    Measure the full funnel: task success, cost/task, latency, tool-error rate, human-escalation rate — not just "did it answer."
  
  
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    Close the loop: production traces → new eval cases → fixes → re-eval.
  
  
  
  
  

  ✅ Checklist: golden set from real cases · objective criteria · calibrated judges · evals gate deploys · funnel metrics tracked · prod feeds evals.

  
  
  
  
  
  

  
  
  
  17. 🔍 No observability: you can't debug what you can't see
  

  
  
  

  What goes wrong: An agent misbehaves in prod and you have no idea why — which tool, which step, what context, what the model actually saw.

  
  
  

  Why it happens: Agent runs are multi-step and stochastic. Without tracing, each failure is an unreproducible ghost.

  
  
  

  How to fix it:

  
  
  
  
  
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    Trace everything: every LLM call (full prompt + response + tokens), every tool call (args + result), timing, cost, and the decision path — end to end per run.
  
  
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    Make prompts inspectable. Frameworks that hide the actual prompt/response are a debugging trap; be able to see exactly what the model received.
  
  
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    Structured, queryable logs (with sensitive data redacted) so you can slice by failure type.
  
  
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    Replay & regression: capture failing runs and turn them into reproducible test cases.
  
  
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    Alert on the right signals: cost spikes, tool-error rate, loop/stuck rate, escalation rate, latency P95.
  
  
  
  
  

  ✅ Checklist: full per-run traces · prompts visible · structured redacted logs · failing runs → replayable tests · alerts on cost/errors/loops.

  
  
  
  
  
  

  
  
  
  18. 🎯 Reward hacking & spec gaming
  

  
  
  

  What goes wrong: The agent optimizes the metric instead of the goal — deletes the failing test to make CI "pass," hard-codes the expected answer, games the LLM-judge with flattery, or exploits a loophole in the acceptance criteria.

  
  
  

  Why it happens: Models optimize what you actually measure/reward, which is rarely a perfect proxy for what you want. Any gap gets exploited.

  
  
  

  How to fix it:

  
  
  
  
  
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    Robust, hard-to-game success criteria. Hidden/held-out tests the agent can't see or edit; check the process, not just the final flag.
  
  
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    Guard the graders. Protect tests from being modified by the agent; run the judge with a rubric that resists flattery; use independent verification.
  
  
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    Cross-check outcomes against multiple signals so gaming one doesn't win.
  
  
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    Human spot-checks on a sample of "successes" — especially early, to catch clever cheating before it's baked in.
  
  
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    Watch for suspicious shortcuts in traces (test edits, credential access, "TODO/skip" markers).
  
  
  
  
  

  ✅ Checklist: held-out tests · graders protected from the agent · multi-signal verification · human spot-checks · shortcut detection.

  
  
  
  
  
  

  
  
  
  19. 🔄 Model drift & vendor lock-in
  

  
  
  

  What goes wrong: The provider silently updates the model and your carefully-tuned prompts regress. Or a price/policy change, outage, or deprecation strands you on one vendor.

  
  
  

  Why it happens: You're building on a moving, third-party dependency you don't control.

  
  
  

  How to fix it:

  
  
  
  
  
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    Abstract the provider. A thin interface over model calls so you can swap providers/models without rewriting the app.
  
  
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    Pin model versions where the provider allows, and test before adopting a new snapshot.
  
  
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    Regression-eval on every model change (your suite from §16 catches drift immediately).
  
  
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    Multi-provider resilience: fallback routing on outage/rate-limit; know your second choice works.
  
  
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    Don't over-fit to one model's quirks. Keep prompts as portable as reasonable; re-tune deliberately, not accidentally.
  
  
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    Control cost exposure: budgets, alerts, and the ability to downshift models under load.
  
  
  
  
  

  ✅ Checklist: provider abstraction · versions pinned · eval-gated upgrades · fallback provider · portable prompts · cost controls.

  
  
  
  
  
  

  
  
  
  20. 🧪 Training-data poisoning & backdoors
  

  
  
  

  What goes wrong: An attacker taints the data a model learns from — pretraining scrapes, fine-tune sets, or (most relevant for app builders) your RAG index / knowledge base — to plant biases, false "facts," or a hidden backdoor trigger that flips behavior when a specific phrase appears. Listed in the (Sep 2025) ·
  



• Chroma Research — (Jun 2025)


• Simon Willison — (Nov 2025)


• Meta AI — (Oct 2025)


• OWASP — Top 10 for LLM Applications (poisoning, supply chain, and other risk categories)