From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence

Jiahong Qian

cs.CLOct 14, 2026

From Unreliable to Usable: Decomposing Failure Modes and Confidence Signals in LLM Entity Extraction - Evidence from Cyber Threat Intelligence

Authors: Jiahong Qian

Organizations: Shanghai Jian Qiao University, Shanghai, China

Abstract

Audits of large language model (LLM) extraction from full-length documents report severe unreliability, leaving two questions open: what the failures actually consist of, and whether confidence signals can turn unreliable output into usable output. We run a preregistered, fully frozen evaluation of two new-generation low-cost LLMs (DeepSeek-V4-Flash, GLM-5.3-Flash) with a flagship-tier control (GPT-6.1-Sol) on a public corpus of 354 incident campaigns (315 unique reports, 630 model-report evaluations), scoring extraction quality against gold, confidence calibration, and robustness to single-sentence perturbations. Three findings emerge. (1) Unreliability persists across tiers: micro precision is 0.03-0.34 and verbalized calibration error (ECE) 0.53-0.94 across both splits; the gap does not close at the flagship tier. (2) The dominant failure is not hallucination: a full mention census plus a three-tier annotation of 360 sampled candidates (two independent LLM annotators from vendors outside the evaluated set, Cohen's kappa = 0.79 on the preregistered acceptance line, fully disclosed) back-projects to 65.6% background mentions and 19.9% gold-scope gaps, with only 1.2% unsupported candidates. (3) Confidence ranks but does not verify: zero-cost self-consistency-frequency tiering passes a preregistered top-30% precision-gain bar in all six test-split model-type cells (4/6 on train), the zero-cost text-frequency x SC frequency combination wins 3, ties 2, loses 1 across cells, yet relation-level counterfactuals on all 45 targets show that a single misattribution sentence flips high-confidence judgments in 95.6-100% of cases, and even a background sentence explicitly marked as unrelated still binds a fake vulnerability in up to 26.7% of them. We conclude with a deployment boundary: confidence tiering may set review priorities, never truth.

Figures & tables

Model	Type	Micro P	Micro R	Micro F1	Macro F1	95% CI (macro F1)
GLM-5.3-Flash (test, 93)	APT	0.284	0.978	0.441	0.645	[0.576, 0.714]
	CVE	0.069	0.884	0.129	0.225	[0.156, 0.295]
	AV	0.174	0.806	0.287	0.309	[0.248, 0.373]
DeepSeek-V4-Flash (test, 93)	APT	0.336	0.957	0.497	0.675	[0.602, 0.743]
	CVE	0.062	0.855	0.115	0.224	[0.154, 0.292]
	AV	0.297	0.806	0.434	0.391	[0.314, 0.469]

Table 1: Multiset entity-extraction PRF, test split (train-split rows are analogous; appendix). High recall with low precision is structural: the models find most gold entities but drown them in extra candidates (GLM-5.3-Flash test emits 15.1 CVE strings per report on average against at most 2 gold primary CVEs).

Failure mode	$n$	$n$ %	w%
Background mention (L2 $=$ no)	134	37.2	65.6
Event-relevant, not in gold	120	33.3	19.9
TP (relevant, bound, in gold)	52	14.4	10.0
No textual support (lower bound)	37	10.3	1.2
Gold-matched, not event-relevant	11	3.1	2.7
Unresolved borderline	5	1.4	0.4

Table 2: Three-tier failure-mode distribution on the 360-row stratified subsample, all three entity types (w% = back-projected to the full FP composition via sampling weights; the top rows are FP-composition shares). Relation misbinding is a 1-row residual in natural output.

Figure 1: Coverage–accuracy curves of confidence tiering (test split): rows are GLM-5.3-Flash and DeepSeek-V4-Flash; columns are APT / CVE / AV. Orange circles: verbalized self-report; blue squares: SC frequency; gray dotted: overall accuracy. SC-frequency tiering is steep and monotone (e.g. DeepSeek-V4-Flash APT precision 0.500 at 30% coverage vs overall 0.321); verbalized is nearly flat (97%+ of self-reports pile into 80–100).

Model	Type	rand.	text_freq	verb.	sc_freq
DeepSeek-V4-Flash	APT	$-.004{\pm}.045$	+0.367	+0.042	+0.179
	CVE	$\pm$ .011	+0.084	+0.063	+0.118
	AV	$\pm$ .037	$-$ 0.034	+0.066	+0.153
GLM-5.3-Flash	APT	$\pm$ .039	+0.340	+0.319	+0.225
	CVE	$\pm$ .013	+0.082	+0.109	+0.131
	AV	$\pm$ .024	$-$ 0.063	+0.076	+0.157

Table 3: Unified-candidate-set ranking baselines, test split: top-30% precision gain against the preregistered

+0.10

bar (random = 200 seeds

\pm

sd). The zero-cost combination textfreq_sc (omitted column; Fig. 2 ) wins 3, ties 2, loses 1 of 6 cells; the flagship tier confirms the mechanism at textfreq_sc

+0.454

on APT, the largest value across all 9 combinations.

Figure 2: Ranking baselines including the zero-cost combination textfreq_sc : on actor entities mention strength measures event centrality; on techniques text frequency is negative while SC works. Red dashed line: preregistered $+0.10$ bar.

Perturbation	Outcome	DeepSeek-V4-Flash	GLM-5.3-Flash
neg (retraction)	true CVE abandoned	34.1%	76.2%
bg (unrelated)	fake CVE bound to event	26.7%	4.5%
mis (attribution)	injected CVE bound	95.6%	100%
mis (attribution)	injected org. accepted	73.3%	95.6%
Control: assertion (v1), fake CVE bound		86.7%	97.8%

Table 4: Relation-level counterfactuals: one injected sentence per report (all 45 targets, both models). Retained high confidence after binding: verbalized 58–98, SC frequency 0.50–0.90.

Appendix figures & tables5 assets

Supplementary material from the paper’s appendix.

Appendix

Batch	Reports	Used calls	Input tok	Output tok
GLM-5.3-Flash train	222	2,202	17.10M	2.99M
GLM-5.3-Flash test	93	822	5.64M	3.04M
DeepSeek-V4-Flash train	222	2,202	17.24M	5.81M
DeepSeek-V4-Flash test	93	822	5.64M	4.31M
ext main run (30 $\times$ 2)	60	258	2.05M	1.25M
ext counterfactual (15 $\times$ 2 $\times$ 2)	60	60	0.46M	0.29M

Appendix

Table 5: Usage and reproducibility metadata. Per-report call metadata (models/gateways/usage_by_gateway) is archived per batch; deterministic task ids guarantee idempotent resume without double billing; ext batches are physically separated under their own task-id prefix. Revision-round additions: relation-level counterfactuals (1,080 planned calls each for DeepSeek-V4-Flash and GLM-5.3-Flash), verification baselines (DeepSeek-V4-Flash 1,221 / GLM-5.3-Flash 1,200 calls), and the flagship control (822 calls, list-price

\approx

$28.5).

Model	Type	vECE	sECE	vGain	sGain
GLM-5.3-Flash train	APT	0.534	0.259	+0.328	+0.298
	CVE	0.851	0.165	+0.041	+0.063
	AV	0.694	0.244	+0.120	+0.151
DeepSeek-V4-Flash train	APT	0.652	0.253	+0.168	+0.405
	CVE	0.938	0.158	+0.022	+0.055
	AV	0.675	0.329	+0.018	+0.221

Appendix

Table 6: Train-split calibration (full). CVE verbalized ECE train 0.94 vs. test 0.837 (GLM-5.3-Flash) / 0.877 (DeepSeek-V4-Flash): caused by the higher FP density and lower correctness of train-split CVEs; the direction is consistent.

#	Campaign	Blocked at
1	APT41__2016-07	rag / cot
2	APT41__2017-07	rag / cot / rag_cot
3	APT41__2018-07	rag / cot
4	CopyKittens__2017-03	rag / cot
5	CopyKittens__2017-04	rag / cot
6	Deep Panda__2014-04	cot

Appendix

Table 7: Content-filtering gap in the ablations (15 reports, one vendor channel; finish_reason = sensitive / content_filter ). Neither the DeepSeek-V4-Flash channel nor the GLM-5.3-Flash main-run channel saw any blocking; ablation figures use the joint 84-report scope.

Model	Config	APT macro-F1 ( $\Delta$ )	CVE ( $\Delta$ )	AV ( $\Delta$ )	mean out tok
DeepSeek-V4-Flash	base@90	0.679 [.603,.747]	0.219 [.156,.286]	0.393 [.316,.474]	464
DeepSeek-V4-Flash	rag_k3	0.711 (+0.032) [.631,.775]	0.226 (+0.007)	0.343 ( $-$ 0.051) [.262,.425]	543
DeepSeek-V4-Flash	rag_k6	0.704 (+0.025) [.626,.777]	0.226 (+0.007)	0.378 ( $-$ 0.016) [.292,.465]	566
DeepSeek-V4-Flash	rag_k12	0.703 (+0.025)	0.227 (+0.008)	0.383 ( $-$ 0.011)	—
DeepSeek-V4-Flash	cot3	0.691 (+0.013) [.613,.770]	0.233 (+0.014)	0.311 ( $-$ 0.083) [.239,.396]	430
DeepSeek-V4-Flash	cot5	0.678 (+0.000) [.597,.762]	0.220 (+0.001)	0.281 ( $-$ 0.112) [.210,.356]	481

Appendix

Table 8: Second-stage ablation:

\Delta

macro-F1 / absolute values vs. the same model’s base (@90-report intersection; 95% bootstrap CI where shown; omitted CIs are of the same magnitude as adjacent same-model rows, full CIs in ablation_summary.json ). Token column is DeepSeek-V4-Flash-side measured; GLM-5.3-Flash-side thinking cannot be disabled (output tokens 5–6

\times

). schema_noev reports only PRF. Flagship scope: ablations were not run on GPT-6.1-Sol (repair-path conclusions scoped to the flash tier).

Hypothesis	Status	Evidence
H1 three-tier annotation	completed & frozen	§ 4.2 , Table 2
H2 SC increment (ctrl. text freq.)	completed	§ 4.4
H3 review budget + audit-label recalib.	completed	§ 4.4
H4 gold-bearing extrapolation	completed	§ 4.6
H5 review simulation vs. human effort	compl. w/ scope limits	Limitations
H6 third-party calibrated verifier	completed	§ 4.5

Appendix

Table 9: Preregistered hypothesis register (H1–H6), final status. All acceptance lines and mechanisms were registered before evaluation; failing a registered bar must be restated as a negative result.

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