AI Threat AlertCost-Minimized Label-Flipping Poisoning Attack to LLM Alignment
preserving the intended poisoning effect. Empirical results demonstrate that this cost-minimization post-processing can significantly reduce poisoning costs over baselines, particularly when the reward model's feature dimension
Self-Purification Mitigates Backdoors in Multimodal Diffusion Language Models
induced behaviors and restore normal functionality. Building on this, we purify the poisoned dataset using the compromised model itself, then fine-tune the model on the purified data to recover
Enhanced Detective: A Dual-Stage Proactive Defense Against Internal Poisoning Attacks in Financial Fraud Detection Using Federated Learning
However, FL schemes are prone to security vulnerabilities, where malicious clients can poison local data or model updates and degrade the global model, and hence the performance of the resulting
GradSentry: Gradient Spectral Entropy for Backdoor Sample Filtering in Large Language Model Fine-Tuning
Fine-tuning Large Language Models with untrusted data exposes models to backdoor attacks, where poisoned samples cause targeted misbehavior. Existing sample-filtering defenses rely on clustering, which requires sufficient data
Merging Triggers, Breaking Backdoors: Defensive Poisoning for Instruction-Tuned Language Models
backdoor attacks, where adversaries poison a small subset of data to implant hidden behaviors. Despite this growing risk, defenses for instruction-tuned models remain underexplored. We propose MB-Defense (Merging
Oracle Poisoning: Corrupting Knowledge Graphs to Weaponise AI Agent Reasoning
models from three providers (N=30 per model), where models autonomously invoke a graph query tool and reason from results. The result is unambiguous: every tested model trusts poisoned data
Why LoRA Fails to Forget: Regularized Low-Rank Adaptation Against Backdoors in Language Models
large language models, but it is notably ineffective at removing backdoor behaviors from poisoned pretrained models when fine-tuning on clean dataset. Contrary to the common belief that this weakness
Multi-Agent Framework for Threat Mitigation and Resilience in AI-Based Systems
making them targets for data poisoning, model extraction, prompt injection, automated jailbreaking, and preference-guided black-box attacks that exploit model comparisons. Larger models can be more vulnerable to introspection
Backdoor Mitigation in Decentralized LLM Fine-Tuning
chosen output whenever a secret trigger appears. We show that a single node poisoning its own model can backdoor adapters of nodes that have never seen a poisoned example, making
AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective
emph{data decryption attacks and watermark removal attacks}; (2) Data$\rightarrow$Model (D$\rightarrow$M): including \emph{poisoning, harmful fine-tuning attacks, and jailbreak attacks}; (3) Model$\rightarrow$Data
TEMPO-Diffusion: Temporally Exposed Malicious Poisoning of Diffusion Models
Noise-based backdoor attacks on diffusion models typically rely on input-time trigger injection, untargeted activation, and out-of-distribution target generation. Such assumptions reduce both the stealthiness
Silent Sabotage During Fine-Tuning: Few-Shot Rationale Poisoning of Compact Medical LLMs
poisoning attack targeting the reasoning process of medical LLMs during SFT. Unlike backdoor attacks, our method injects poisoned rationales into few-shot training data, leading to stealthy degradation of model
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
high volume of novel threats. This work investigates a realistic gray-box poisoning threat model targeting these pipelines. Using the secml_malware framework, we generate problem-space adversarial binaries through
From Poisoned to Aware: Fostering Backdoor Self-Awareness in LLMs
triggers responsible for misaligned outputs. Guided by curated reward signals, this process transforms a poisoned model into one capable of precisely identifying its implanted trigger. Surprisingly, we observe that such
AntiFLipper: A Secure and Efficient Defense Against Label-Flipping Attacks in Federated Learning
remains vulnerable to label-flipping attacks, where malicious clients manipulate labels to poison the global model. Despite their simplicity, these attacks can severely degrade model performance, and defending against them
Once Poisoned, Arbitrarily Controlled: A Programmable Backdoor in VLMs
target selection from poisoning, enabling dynamic control of target captions without retraining the VLM. Our method has two components. First, a heuristic poisoning strategy exposes the model to diverse trigger
Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem
could share and distribute seemingly benign LoRA plugins that contain hidden functionalities to poison the model-sharing market, like Civitai or Liblib, severely undermining the user trust that underpins this
Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can poison. Existing poisoning attacks primarily rely on fixed trigger phrases that defenses such as outlier detection, clean
DuCodeMark: Dual-Purpose Code Dataset Watermarking via Style-Aware Watermark-Poison Design
suspicious rate $\leq$ 0.36), robustness against both watermark and poisoning attacks (recall $\leq$ 0.57), and a substantial drop in model performance upon watermark removal (Pass@1 drops by 28.6%), underscoring
Unveiling the Security Risks of Federated Learning in the Wild: From Research to Practice
perspective. We systematize three major sources of mismatch between research and practice: unrealistic poisoning threat models, the omission of hybrid heterogeneity, and incomplete metrics that overemphasize peak attack success while