A malicious retrieved document need not demand that an assistant abandon its task. It can preserve the report, reply or API operation while modifying a recipient, number, cited instruction or final parameter. This makes outcome plausibility a weak detection criterion. The paper compares near-target objectives with fixed-target prompt injections across…
Inspect the ClaimsCase · DiggingBeagle record
Near-target prompt injections preserve the main task while changing a critical detail
NTBench measures attacks that alter a small part of an otherwise plausible answer or agent action. In its tested configurations, near-target objectives outperform fixed-target attacks, without showing production harm.
The benchmark results depend on selected targets and three attempts per sample. The paper does not measure monetary losses, actual unauthorized changes or all deployed agents.
Assessment profile
Assessment method- Setting
- evaluation
- Exploitation
- demonstrated
- Evidence
- Grade B
- Remediation
- not applicable
- AI role
- AGAINST AI
Basis and provenance
Enrich update: CLM-ENRICH-20261027-22 cites SRC-2026-CP21-NTBENCH-NEAR-TARGET-INJECTION at Abstract, reference-derived targets and exclusion of uninduced deviations. New evidence concerns the bounded reported mechanism, prerequisite or outcome; setting evaluation; exploit status demonstrated; remediation status not_applicable. No independently comparable impact scale, affected production population or universal exploit success measure was established, so the impact band remains null. Original Claims remain individually attributable and any observed incident or simulated result is distinguished in the narrative.
Assessed Oct 27, 2026 using diggingbeagle.assessment/1.
Evidence basis: CLM-ENRICH-20261027-22 · NTBench: near-target prompt injection against correct-looking task outcomes
At a glance
A safe-looking completion may still be a successful injection when the attacker controls one critical field.
Read the implicationsThe benchmark results depend on selected targets and three attempts per sample. The paper does not measure monetary losses, actual unauthorized changes or all deployed agents.
Limits and uncertaintyFull account
A malicious retrieved document need not demand that an assistant abandon its task. It can preserve the report, reply or API operation while modifying a recipient, number, cited instruction or final parameter. This makes outcome plausibility a weak detection criterion.
The paper compares near-target objectives with fixed-target prompt injections across seven LLMs, ten datasets and two agent benchmarks, reporting larger measured deviation rates for the close-to-correct attack targets. The benchmark results depend on selected targets and three attempts per sample. The paper does not measure monetary losses, actual unauthorized changes or all deployed agents.
Compare consequential tool parameters against the original authorized objective and an independently sourced reference, rather than merely checking whether the broad task appears complete. A safe-looking completion may still be a successful injection when the attacker controls one critical field. Primary evidence.
Additional evidence and trust boundary
A near-target payload aims to preserve most of the requested work so that a conventional task-completion grader still sees a plausible result. The vulnerability is at the transition from low-trust retrieved material into exact outputs or tool arguments, such as a changed recipient or amount. Compare final effects against the user's authorized target bytes and test both incorrect and harmful deviations; a generic refusal metric will not measure these near misses. Supporting record.
Mechanism and trust boundary
- 01
Step
- 02
Step
- 03
Step
Timeline
- Oct 23, 2026disclosure
Public disclosure
Claims & evidence
CLM-ENRICH-20261027-22NTBench checks deviations from a task-specific reference and excludes mistakes already present without injection, separating maliciously induced near-target changes from baseline agent error.supported
Basis: reported finding
- supportsNTBench: near-target prompt injection against correct-looking task outcomesprimary disclosure
Abstract, reference-derived targets and exclusion of uninduced deviations
CLM-CP21-NTBENCH-NEAR-TARGET-INJECTION-1The paper compares near-target objectives with fixed-target prompt injections across seven LLMs, ten datasets and two agent benchmarks, reporting larger measured deviation rates for the close-to-correct attack targets.supported
Basis: reported finding
- supportsNTBench: near-target prompt injection against correct-looking task outcomesprimary disclosure
Abstract; attack-target construction and controlled evaluation
Implications
A safe-looking completion may still be a successful injection when the attacker controls one critical field.
Controls and mitigations
- Compare consequential tool parameters against the original authorized objective and an independently sourced reference, rather than merely checking whether the broad task appears complete.
Unknowns and contradictions
- The benchmark results depend on selected targets and three attempts per sample. The paper does not measure monetary losses, actual unauthorized changes or all deployed agents.
Sources and citation
Material revision history
- Oct 27, 2026 · Canonical change recorded · new in release · revision 196