VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

Yurong Hao; Wen Zhou; Guowei Guan; Tiantong Wu; Fuyao Zhang; Wei Yang Bryan Lim

cs.CRSep 29, 2026

VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents

Authors: Yurong Hao, Wen Zhou, Guowei Guan, Tiantong Wu, Fuyao Zhang, Wei Yang Bryan Lim

Organizations: College of Computing and Data Science, Nanyang Technological University

Abstract

Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it simultaneously introduces a systemic vulnerability: adversarial evidence injected into a single agent can be rationalised into a legitimate preference narrative, written back into memory, and propagated to other agents through interaction contexts. We term the local rationalisation process reflection laundering, and its system-wide escalation through collaborative reflection collaborative-reflection hijacking. Existing attacks on recommender systems, whether based on interaction-level data poisoning or text-level adversarial perturbations, assume static pipelines and thus cannot exploit this recurrent, multi-agent amplification pathway. To bridge this gap, we first conduct a controlled vulnerability analysis that establishes two exploitable properties underlying collaborative-reflection hijacking: reflective persistence and cross-agent propagation. Then building on these findings, we propose VirusCascade, the first black-box targeted promotion attack that jointly shapes semantic and structural attack surfaces: the former ensures the target item is naturally rationalised as satisfying broad user preferences, the latter positions it for system-wide propagation. Extensive experiments on four real-world datasets across diverse LLM-ARS architectures demonstrate that VirusCascade consistently achieves state-of-the-art targeted exposure under evaluated stealth constraints, reaching a mean E@20 of 0.384 and surpassing the strongest baseline by an absolute margin of +0.185.

Figures & tables

Fig. 1: RS vs. LLM-ARS. Traditional RS (Left) optimises fixed user–item representations through gradient-based learning, whereas LLM-ARS instantiate users and items as memory-equipped agents that update their semantic states through autonomous interaction and collaborative reflection.

Metric	Lag 1	Lag 2	Lag 3	Lag 4
Reuse Rate	78.0%	81.7%	79.3%	81.6%
Elaboration Rate	0 3.3%	0 3.4%	16.7%	0 6.3%
Avg. n (admitted users)	30	29	18	2
Baseline regeneration rate (no-probe control): 2.0%

TABLE I: Reflective persistence among admitted users (AgentCF, CDs & Vinyl), averaged over 10 runs. Each column reports a lag relative to the user’s individual admission round.

Fig. 2: Cumulative fraction of benign users measured across interaction-reflection rounds. Dark bars: newly admitted in each round; light bars: admitted in prior rounds and still retained. Dashed red line: no-probe baseline regeneration rate (2%).

Users	$n$	Setting	Rec. Drift $\uparrow$	Claim Adoption $\uparrow$
Hop-1	12	Sem.-null	0.003 [0.001, 0.005]	0.0% [0.0, 0.0]
Hop-1	12	Sem.-probe	0.397 [0.371, 0.423]	33.6% [28.9, 38.3]
Hop-2	25	Sem.-null	0.002 [0.001, 0.003]	0.0% [0.0, 0.0]
Hop-2	25	Sem.-probe	0.245 [0.221, 0.269]	25.8% [22.7, 28.9]
Hop $>2$	63	Sem.-null	0.003 [0.002, 0.004]	0.0% [0.0, 0.0]
Hop $>2$	63	Sem.-probe	0.123 [0.112, 0.134]	6.7% [5.1, 8.3]

TABLE II: Cross-agent propagation under the semantic probe and topology-matched semantic-null control. Values report the mean and 95%

t

-confidence interval across 10 paired runs;

n

denotes the number of benign users in each hop group.

Fig. 3: Overview of VirusCascade . The adversary extracts preference motifs from anchor items and designs the target profile to make

i^{*}

easier to rationalise during reflection. It then constructs smooth, propagation-oriented behaviour trajectories from anchor items to

i^{*}

. The victim system’s collaborative reflection may subsequently admit and propagate the injected evidence.

Dataset	#Users	#Items	#Inter.	Sparsity
CDs & Vinyl	112,395	73,713	1,443,755	99.98%
Movies & TV	297,529	60,110	3,404,812	99.98%
Automotive	193,651	79,317	1,709,025	99.99%
Musical Instruments	27,530	10,611	231,312	99.92%

TABLE III: Statistics of the datasets used in our experiments.

Victim RS	AgentCF
Attack	CDs & Vinyl						Movies & TV
Attack	H@10	N@10	E@10	H@20	N@20	E@20	H@10	N@10	E@10	H@20	N@20	E@20
NoAttack	0.130	0.050	(-)	0.260	0.082	(-)	0.220	0.125	(-)	0.310	0.148	(-)
RandAttack	0.090	0.053	0.010	0.210	0.084	0.071	0.170	0.110	0.050	0.280	0.138	0.120
PopAttack	0.115	0.053	0.000	0.235	0.077	0.010	0.170	0.114	0.050	0.290	0.133	0.150
ExpPromot	0.140	0.054	0.000	0.290	0.091	0.051	0.185	0.117	0.030	0.270	0.134	0.110

TABLE IV: Attack performance across three victim architectures on CDs & Vinyl and Movies & TV datasets. We report H@K and N@K for recommendation utility and E@K for targeted promotion effectiveness. NoAttack denotes the same victim configuration and benign protocol without any adversarial manipulation, and we report its E@K as (-).

Dataset	NoAttack		DrunkAgent	Ours
Dataset	Range	Mean	DrunkAgent	Ours
CDs & Vinyl	[10.31, 90.02]	28.78	49.93	32.22
Movies & TV	[12.91, 374.28]	72.85	50.65	39.35

TABLE V: Attack imperceptibility in terms of perplexity.

Attacks	CDs & Vinyl			Movies & TV
Attacks	@1	@2	@L	@1	@2	@L
TextFooler	0.200	0.000	0.200	0.251	0.000	0.121
DrunkAgent	0.294	0.000	0.100	0.175	0.000	0.087
VirusCascade	0.321	0.109	0.268	0.294	0.090	0.157

TABLE VI: Attack imperceptibility in terms of ROUGE.

Attacks	Flagged as Manipulated $\downarrow$	Factual Consistency $\uparrow$
Original	19.3%	$4.31\pm 0.47$
DrunkAgent	28.7%	$4.02\pm 0.95$
VirusCascade	21.9%	$4.16\pm 0.62$

TABLE VII: Attack imperceptibility in terms of human evaluation (20 participants, 120 descriptions each).

Victim RS	AgentCF
Variant	CDs & Vinyl						Movies & TV
Variant	H@10	N@10	E@10	H@20	N@20	E@20	H@10	N@10	E@10	H@20	N@20	E@20
NoAttack	0.130	0.050	(-)	0.260	0.082	(-)	0.220	0.125	(-)	0.310	0.148	(-)
w.o SemInj	0.110	0.058	0.010	0.250	0.094	0.071	0.200	0.135	0.020	0.320	0.164	0.170
w.o StruInj	0.100	0.046	0.020	0.260	0.086	0.131	0.160	0.101	0.000	0.300	0.135	0.080
VirusCascade	0.120	0.057	0.111	0.250	0.083	0.374	0.205	0.123	0.070	0.305	0.138	0.240

TABLE VIII: Ablation study: effect of removing semantic injection (w.o SemInj) or structural injection (w.o StruInj).

Fig. 4: Effect of malicious user fraction $m$ .

Fig. 5: Effect of the coefficient

\alpha

Dataset	Victim RS	LLaMA-3			GPT-4o			Gemini-2.5
Dataset	Victim RS	H@20	N@20	E@20	H@20	N@20	E@20	H@20	N@20	E@20
CDs & Vinyl	AgentCF	0.320	0.107	0.151	0.250	0.083	0.374	0.340	0.119	0.333
	AgentSEQ	0.410	0.129	0.160	0.240	0.087	0.465	0.330	0.112	0.283
	AgentRAG	0.320	0.096	0.170	0.230	0.105	0.545	0.350	0.115	0.364
Movies & TV	AgentCF	0.250	0.102	0.140	0.290	0.134	0.240	0.340	0.161	0.140
	AgentSEQ	0.330	0.144	0.150	0.300	0.142	0.300	0.340	0.165	0.210

TABLE IX: Attack transferability with different auxiliary models

\mathcal{M}_{\mathrm{aux}}

Dataset	$\mathcal{M}_{\mathrm{aux}}$	PPL		ROUGE
Dataset	$\mathcal{M}_{\mathrm{aux}}$	NoAttack	Ours	@1	@2	@L
CDs & Vinyl	LLaMA-3	[21.64, 107.62]	56.28	0.267	0.103	0.267
	GPT-4o	[10.31, 90.02]	32.22	0.321	0.109	0.268
	Gemini-2.5	[11.78, 113.24]	31.82	0.341	0.125	0.268
Movies & TV	LLaMA-3	[13.75, 409.59]	76.20	0.412	0.091	0.206
	GPT-4o	[12.91, 374.28]	39.35	0.294	0.090	0.157

TABLE X: Attack imperceptibility with different auxiliary models

\mathcal{M}_{\mathrm{aux}}

. NoAttack PPL is reported as [min, max].

Appendix figures & tables6 assets

Supplementary material from the paper’s appendix.

Appendix

Marker	No-Probe Regen.	Lag-1	Lag-2	Lag-3	Lag-4
Rare	2.0%	78.0%	81.7%	79.3%	81.6%
Common	15.0%	72.0%	73.4%	68.7%	76.4%

Appendix

TABLE XI: Reflective persistence under rare and common-frequency diagnostic markers. Lag columns report reuse rate.

Victim RS	AgentCF (user-only)
Attack	CDs & Vinyl						Movies & TV
Attack	H@10	N@10	E@10	H@20	N@20	E@20	H@10	N@10	E@10	H@20	N@20	E@20
NoAttack	0.170	0.074	(-)	0.350	0.119	(-)	0.190	0.124	(-)	0.320	0.157	(-)
TextBugger	0.120	0.057	0.010	0.370	0.119	0.061	0.220	0.133	0.020	0.320	0.158	0.130
TextFooler	0.150	0.063	0.040	0.340	0.111	0.051	0.210	0.115	0.010	0.330	0.145	0.110
DrunkAgent	0.140	0.069	0.000	0.270	0.102	0.030	0.220	0.129	0.020	0.340	0.160	0.100

Appendix

TABLE XII: Attack transferability under user-only agent architectures (no item-side memory updates).

Victim RS	AgentCF
Attack	Automotive						Musical Instruments
Attack	H@10	N@10	E@10	H@20	N@20	E@20	H@10	N@10	E@10	H@20	N@20	E@20
NoAttack	0.150	0.075	(-)	0.230	0.094	(-)	0.110	0.042	(-)	0.280	0.085	(-)
RandAttack	0.080	0.043	0.000	0.180	0.068	0.170	0.060	0.023	0.010	0.160	0.049	0.141
PopAttack	0.070	0.038	0.050	0.230	0.078	0.170	0.090	0.043	0.030	0.220	0.075	0.091
ExpPromot	0.100	0.043	0.030	0.230	0.076	0.180	0.030	0.011	0.051	0.150	0.040	0.152

Appendix

TABLE XIII: Attack performance on Automotive and Musical Instruments. H@K and N@K measure recommendation utility, while E@K measures targeted promotion effectiveness.

Fig. 6: Population-scale evaluation on CDs & Vinyl under a fixed attacker budget of five users. Bars report VirusCascade as the active population increases from 100 to 500 users. Star markers denote DrunkAgent under the corresponding population setting.

Fig. 7: Attack performance over an extended 20-round interaction on AgentCF with CDs & Vinyl. Top: recommendation utility (H@20); bottom: targeted exposure (E@20).

Defence	Attack	AgentCF			AgentSEQ			AgentRAG
Defence	Attack	H@20	N@20	E@20	H@20	N@20	E@20	H@20	N@20	E@20
ONION (T)	NoAttack	0.410	0.141	(-)	0.410	0.161	(-)	0.410	0.168	(-)
	DrunkAgent	0.310	0.107	0.030	0.380	0.135	0.030	0.400	0.131	0.040
	VirusCascade	0.340	0.128	0.256	0.370	0.130	0.202	0.350	0.140	0.202
Fraudar (B)	NoAttack	0.270	0.088	(-)	0.380	0.124	(-)	0.340	0.114	(-)
	DrunkAgent	0.290	0.109	0.061	0.390	0.145	0.081	0.380	0.138	0.061

Appendix

TABLE XIV: Attack performance under text- (T), behaviour- (B), and memory-level (M) defences on the CDs & Vinyl dataset. Comparisons are made within each defence block, as defences alter the inference pipeline.

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