It reduces hallucination but does not stop it. Grounding gives the model trusted text to answer from, which helps with freshness and specifics. The model can still misread the text, ignore it, or fill gaps beyond it. A grounded answer still needs spot checks against the quoted source.
What grounding means
Grounding, often done through retrieval augmented generation, means fetching relevant trusted documents and placing them in the prompt so the model answers from them. OWASP lists this first among its mitigations for misinformation: ground responses in verified information from trusted external sources.
For security work, trusted documents might be advisory records, vendor release notes, your own inventory and your internal policy pages.
What it fixes
- Freshness: answers reflect documents fetched today, not a training cutoff.
- Specificity: version ranges and fixed releases come from the record.
- Traceability: you can ask for the passage relied on.
What it does not fix
| Remaining risk | Example |
|---|---|
| Misreading | Model reverses "fixed in" and "affected before" |
| Ignoring context | Model answers from memory instead of the passage |
| Retrieval miss | The right advisory was never fetched |
| Bad source | The retrieved page is wrong or outdated |
| Injected text | A retrieved page contains instructions the model follows |
The last row matters. NCSC warns that models do not enforce a boundary between instructions and data, so retrieved content is untrusted input and needs permissions limited accordingly.
Checking a grounded answer
- Ask for the exact quote supporting each claim.
- Confirm the quote exists in the source, character for character.
- Confirm the quote says what the answer claims, particularly on ranges and negations.
- Confirm the source is the primary one, not a copy.
- Spot check at least some answers by hand each week, and track error rates.
Designing the retrieval
Keep the document set small and curated. Add dates and source labels to each chunk. Prefer structured fields for versions and ranges over long prose. Test with questions whose answers you know, including ones where the correct answer is "not in the data".
A caution on confidence
A grounded answer reads as more authoritative because it includes quotes. That makes errors more persuasive. Keep the reviewer step even when the format looks rigorous. Related reading: does RAG stop prompt injection, myth vs fact.
Where Safeguard fits
Safeguard maintains its own scanning and advisory matching against your inventory, so the grounding data for your environment comes from analysis of your code, not from model memory. Free scan: https://gold.safeguard.sh/scan.
Quick check
If the answer includes a quote, is it right?
Not necessarily. Check that the quote exists and that the claim matches it.
Is retrieved content trusted?
Treat it as untrusted input. It can contain text that tries to steer the model.
Sources
- OWASP GenAI, LLM09:2025 Misinformation: https://genai.owasp.org/llmrisk/llm092025-misinformation/
- NCSC, Prompt injection is not SQL injection: https://www.ncsc.gov.uk/blog-post/prompt-injection-is-not-sql-injection
Last reviewed: 19 October 2026.
