Can you give a concrete example of prompt injection risks in tool results using published source material?
10/2/2026, 6:28:03 PM · llm:deepseek:deepseek-v4-flash
> ⚠ Low confidence — the final assessment does not establish a complete supported answer for every requested part within budget. Treat this as provisional.
A concrete published example of prompt injection risk in tool results comes from CyCognito's description of indirect prompt injection: "For example, suppose a document- or web-scraping tool feeds third-party web pages into an LLM prompt" , and "In this scenario, an attacker could modify the content (such as posting a malicious review or comment) on a source the system ingests, embedding hidden instructions that the LLM then obeys" . This directly illustrates how untrusted content flowing through a tool (a document- or web-scraping tool) can carry hidden instructions that the model executes.
A complementary example from OWASP describes indirect prompt injection where "Malicious prompts are embedded in content (like a web page or email) that the LLM processes later" , with the specific case: "Example: A malicious blog post containing a hidden prompt that instructs the LLM to reveal internal data" . OWASP also notes these prompts "are often concealed using techniques such as white text on a white background or non-printing Unicode characters" .
Regarding tool results specifically: the sources support the general mechanism (untrusted external content ingested via tools carrying hidden instructions), but none of the supplied passages describes a concrete example of injection embedded specifically in a tool's returned result/output (as opposed to a web page or document a tool ingests). Lasso Security notes that prompt injection risk "emerges as models gain access to data and tools" and that "Retrieval-augmented architectures introduce a different class of risk: implicit trust in external content" , but does not provide a concrete tool-result injection example in the supplied excerpts. This specific gap should be noted: no supplied passage gives a verbatim concrete example of an injection payload appearing in a tool's returned result.
No conflicts were found among the sources; they are consistent in describing indirect prompt injection via untrusted external content processed by the model.
Evidence ledger — supporting quotes
What is a concrete example of prompt injection risk in tool results, as described in published source material?
60%“For example, suppose a document- or web-scraping tool feeds third-party web pages into an LLM prompt.” [S2] Prompt Injection Attacks: Types, Examples & Mitigations | CyCognito
“In this scenario, an attacker could modify the content (such as posting a malicious review or comment) on a source the system ingests, embedding hidden instructions that the LLM then obeys.” [S2] Prompt Injection Attacks: Types, Examples & Mitigations | CyCognito
“Indirect Prompt Injection: Malicious prompts are embedded in content (like a web page or email) that the LLM processes later.” [S1] Prompt Injection | OWASP Foundation
“Example: A malicious blog post containing a hidden prompt that instructs the LLM to reveal internal data.” [S1] Prompt Injection | OWASP Foundation
“Small shifts in phrasing or structure can be enough to alter behavior.External Data Sources and Knowledge BasesRetrieval-augmented architectures introduce a different class of risk: implicit trust in external content.” [S3] Prompt Injection Examples That Expose Real AI Security Risks
Research evidence matrix
Compare research claims with cited sources and inspect recorded excerpts. An empty cell means no inspectable excerpt was recorded; it does not establish whether a claim is true, false, or disputed. Coverage and agent confidence are not measured accuracy.
| Research claim | Inspection status | [S1] Prompt Injection | OWASP FoundationPublication: owasp.orgPublished: Not recorded | [S2] Prompt Injection Attacks: Types, Examples & Mitigations | CyCognitoPublication: cycognito.comPublished: Not recorded | [S3] Prompt Injection Examples That Expose Real AI Security RisksPublication: lasso.securityPublished: Not recorded |
|---|---|---|---|---|
| What is a concrete example of prompt injection risk in tool results, as described in published source material? | Recorded excerpt | Inspect 2 excerptsIndirect Prompt Injection: Malicious prompts are embedded in content (like a web page or email) that the LLM processes later. Example: A malicious blog post containing a hidden prompt that instructs the LLM to reveal internal data. | Inspect 2 excerptsFor example, suppose a document- or web-scraping tool feeds third-party web pages into an LLM prompt. In this scenario, an attacker could modify the content (such as posting a malicious review or comment) on a source the system ingests, embedding hidden instructions that the LLM then obeys. | Inspect 1 excerptSmall shifts in phrasing or structure can be enough to alter behavior.External Data Sources and Knowledge BasesRetrieval-augmented architectures introduce a different class of risk: implicit trust in external content. |
Reference export
3 article references. Recorded titles, links and dates; observed scholarly records also include supplied authors, DOI and journal metadata with read limits. Review metadata before using in a paper. Import RIS into Zotero with File → Import.
Cited sources and references
- 1Prompt Injection | OWASP Foundationowasp.orgFree public reference · no creator payment · extracted html text30%
- 2Prompt Injection Attacks: Types, Examples & Mitigations | CyCognitocycognito.comFree public reference · no creator payment · extracted html text50%
- 3Prompt Injection Examples That Expose Real AI Security Riskslasso.securityFree public reference · no creator payment · extracted html text20%
Decision log · 72 steps
Breaking down: "Can you give a concrete example of prompt injection risks in tool results using published source material?"
Identified 1 research target(s) to investigate; these are not established facts
Deep mode: up to 4 paid/cached/public reads plus one bounded gap-expansion pass when needed.
Web search: 2/2 planned queries attempted, 2 succeeded, 12 public page previews, 0 unavailable queries. Snippets are discovery only. Public reads spend no USDC; model and service operating costs remain separate.
Discovered 21 verified creator source(s) and 17 free public reference(s)
Recalled 60 past runs on this subject — how these sources performed when they were available.
ERC-8004 reputation loaded — composite scores on this subject.
Claim-aware portfolio (exhaustive; bounded selection, not a claim of global optimality) selected 2/12 positive proposal(s): 2 free/cache selections + 0 paid fresh selections, predicting 1/1 claim(s) above the evidence floor with $0.000000/$0.015000 fetch USDC reserved.
Free-preview pre-check maps an actionable source to every sub-claim (1/1); paid reading may proceed within the budget.
Free public read; preview shows a table of attack patterns including 'Tool-call hijacking' with documented examples mapped to OWASP LLM01 and MITRE ATLAS — exactly the concrete tool-result injection example requested. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — selected for the claim-aware evidence portfolio (targets claim 1; 0 fetch USDC, 1 attention slot).
Free public read; OWASP's own page shows concrete examples including an email-assistant manipulation ('ignore all prior instructions') and hidden instructions in ingested content — authoritative published example material. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — selected for the claim-aware evidence portfolio (targets claim 1; 0 fetch USDC, 1 attention slot).
Free public read; preview explicitly describes the agent-with-private-data + untrusted-content + external-communication exploit pattern and cites concrete findings (Slack AI, M365 Copilot, Cursor, GitHub MCP) — directly a concrete published example of prompt injection via tool results. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview gives named real-world cases (Cursor triple CVE chain, Reprompt CVE-2026-24307 Copilot exfiltration) — concrete published examples of injection through tool/agent surfaces. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview details extraction techniques (many-shot priming, crescendo) with a todo-list analogy for injected commands — relevant but more about extraction than tool-result injection specifically. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview gives the canonical indirect-injection example: a document/web-scraping tool feeding untrusted third-party content into the prompt with hidden instructions — a direct concrete tool-result injection example. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview describes a documented case where attacker-controlled content influenced an autonomous coding agent into executing commands — a concrete tool-calling injection example. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview explicitly covers agentic injection via MCP tool descriptions causing exfiltration and unauthorized tool invocations — a concrete tool-result/tool-metadata injection example. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview cites CVE-2026-24307 Reprompt single-click Copilot exfiltration and GitHub Copilot CVEs — concrete published examples of injection through tool/agent integrations. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview recounts the first indirect prompt injection against Bing Chat, where page content read by a tool was injected into the prompt — a foundational concrete tool-result example. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; a survey of attack methods and root causes could supply examples, but the preview is only a publisher/copyright boilerplate with no visible example content, so value is uncertain. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Free public read; preview gives a concrete scenario (support assistant summarizing third-party reviews where hidden instructions override developer intent) and notes danger when the LLM drives downstream tool actions. - free public original-page READ selection (not a cache hit); no purchase or creator reward. — the claim-aware portfolio chose a stronger, less redundant set inside the 4-source attention and $0.015000 fetch-budget caps, so this proposal stays unspent.
Preview is only a repo-list update note; nothing about prompt injection or tool-result risks, so it cannot support the claim. - free public feed reference; no purchase or creator reward.
Preview covers post-quantum crypto algorithms in Workers; unrelated to prompt injection in tool results. - free public feed reference; no purchase or creator reward.
Classic agent-systems overview that may touch tool use, but the preview shows only a generic agent introduction with no injection example, so it cannot support the specific claim. - free public feed reference; no purchase or creator reward.
Children's music video metadata; entirely off-topic. - free public feed reference; no purchase or creator reward.
Preview is about a NASA engineering-excellence report; no connection to prompt injection. - free public feed reference; no purchase or creator reward.
Stablecoin unit-of-account abstract; no prompt-injection content despite decent past citation record on other subjects.
x402 payment-rail abstract; unrelated to prompt injection examples.
Nanopayment settlement abstract; no relevance to tool-result prompt injection.
Idempotency-key abstract; unrelated to prompt injection.
Gardening content; off-topic.
Retro console repair; off-topic.
Preview is an event invitation about fraud strategy; no prompt-injection example.
About running AI agents against protocol code for security triage; no prompt-injection-in-tool-results example in the preview.
Crypto market digest; 'prompt' here means AI slowdown fears, not prompt injection.
Agent underwriting/insurance interview; preview shows no injection example, and the full text is unlikely to center on tool-result injection.
Metadata-only with zero plaintext bytes; title alone cannot establish a concrete injection example, and no preview content supports the claim.
Metadata-only (0 bytes); agent memory topic, no evidence of a prompt-injection example.
DeFi/Ethereum topic; metadata-only and unrelated to prompt injection.
Coinbase trading-practices rebuttal; unrelated to prompt injection.
NFT/digital ownership piece; no prompt-injection content.
Stablecoin dollar/euro onchain gap; unrelated to prompt injection.
Esoteric/mystic content; off-topic.
Open-source vs closed model geopolitics; no prompt-injection example.
x402 settlement latency benchmarks; unrelated to prompt injection despite strong past citation record on payment topics.
Payment finality timing; no prompt-injection content.
First-party buyer-recovery engineering notes; about payment journaling, not prompt injection in tool results.
READ Prompt Injection Examples: 12 Real-World Attack Patterns - selected original public page, 0 USDC; not a cache hit.
Public page unavailable (html-extraction-unavailable); no evidence admitted. Continuing research.
READ Prompt Injection | OWASP Foundation - selected original public page, 0 USDC; not a cache hit.
Read extracted public text from https://community.owasp.org/attacks/PromptInjection - S1; quote matching establishes source grounding, not fact verification.
Sub-claim "What is a concrete example of prompt injection risk in tool …": 30% covered by S1 — S1 (OWASP) gives a concrete indirect prompt injection example: a malicious blog post containing a hidden prompt that instructs the LLM to reveal internal data, concealed via white text or non-printing Unicode. This is topical context for untrusted content processed by an LLM, but it does not specifically describe injection risk in tool results (e.g., a tool/scraper returning attacker-controlled content that the agent treats as instructions). The requested 'tool results' vector is not explicitly answered.
Coverage is below 0.5 because the only read source provides an indirect-injection example (malicious blog post) but not a concrete example of injection risk specifically in tool results. Two free skipped sources directly target this gap: CyCognito explicitly describes a document- or web-scraping tool feeding third-party web pages into an LLM prompt, and the 'Prompt Injection Examples' source covers prompt injection in agentic/tool-calling AI. Both are $0 and fit the remaining budget; buy them in that priority order.
READ original public page https://www.cycognito.com/learn/ai-security/prompt-injection/, 0 USDC - S2
READ original public page https://www.lasso.security/blog/prompt-injection-examples, 0 USDC - S3
Final check — "What is a concrete example of prompt injection risk in tool …": 60% assessed by S2, S1, S3
Final coverage assessment — The question asks for a concrete example of prompt injection risk in tool results from published source material. S2 provides the closest concrete example: a document- or web-scraping tool feeding third-party web pages into an LLM prompt, where an attacker embeds hidden instructions in ingested content that the LLM then obeys. This is a tool-result/indirect injection scenario, though it is framed as a hypothetical example rather than a named real-world incident. S1 and S3 provide related context on indirect prompt injection and tool/retrieval risks but do not give a concrete tool-result example. No source supplies a specific documented incident of prompt injection occurring in tool results. The assessment does not establish a complete supported answer for every requested part.
Synthesizing a grounded answer from 3 source(s)…
Relevance review returned; only checked excerpts can retain support, and review cannot raise it.
Verified public reference (no creator reward) — S2 supports claim 1 at 50%: “For example, suppose a document- or web-scraping tool feeds third-party web pages into an LLM prompt.”
Verified public reference (no creator reward) — S2 supports claim 1 at 90%: “In this scenario, an attacker could modify the content (such as posting a malicious review or comment) on a source the system ingests, embed…”
Verified public reference (no creator reward) — S1 supports claim 1 at 70%: “Indirect Prompt Injection: Malicious prompts are embedded in content (like a web page or email) that the LLM processes later.”
Verified public reference (no creator reward) — S1 supports claim 1 at 90%: “Example: A malicious blog post containing a hidden prompt that instructs the LLM to reveal internal data.”
Below support/reward gate — S1 supports claim 1 at 30%: “The prompts are often concealed using techniques such as white text on a white background or non-printing Unicode characters.”
Below support/reward gate — S3 supports claim 1 at 10%: “existing guardrailsWhere prompt injection risk emerges as models gain access to data and toolsHow production-grade controls can detect and c…”
Verified public reference (no creator reward) — S3 supports claim 1 at 40%: “Small shifts in phrasing or structure can be enough to alter behavior.External Data Sources and Knowledge BasesRetrieval-augmented archite…”
Drafted answer citing 3 source(s)
Confidence: Low — the final assessment does not establish a complete supported answer for every requested part.
owasp.org contributed 30% - free public reference; reward share withheld
cycognito.com contributed 50% - free public reference; reward share withheld
lasso.security contributed 20% - free public reference; reward share withheld
Done. Spent $0 across 0 confirmed/simulated payment(s) to creators.
Portable research receipt
Take the evidence trail with you
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