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  "slug": "jailbreak-economics",
  "edition": "2026-10-24T00",
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  "newsroom": "artificial-intelligence",
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  "newsroom_title": "Artificial Intelligence",
  "angle": "Red teams are pricing their attacks: cost-aware jailbreak metrics, bypassed post-generation checkers, phishing benchmarks for email agents.",
  "headline": "The red teams price their attacks now: jailbreaks measured by cost, post-generation checkers bypassed, email agents phished",
  "dek": "Four security preprints and one practitioner interview point the same way: attack success rates are being repriced by cost, safety checkers are being sidestepped, and agents keep falling for phishing. The defenses are catching up, and being measured too.",
  "published_at": "2026-10-24T01:05:30Z",
  "labels": {
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    "trust_text": "Reported · 5 sources",
    "newsroom": "Artificial Intelligence"
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  "source_count": 5,
  "publishers": [
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    {
      "text": "In a preprint by Rohan Menon and Chih-Hong Cheng, the ASR_STEP metric discounts each victim-model call; re-scored on JailBreakBench, GCG's effectiveness falls 7.0-8.7 percentage points and PAIR's falls 3.4-12.",
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      "text": "In the Mirage preprint, a multimodal jailbreak bypassed post-generation safety checkers in Stable Diffusion 1.5, SDXL, and Kandinsky 2.2 using only limited black-box queries, with no internal model access.",
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      "text": "The AgentPhishing benchmark tested nine LLM email agents: four detected fewer than 10% of adaptive phishing attempts while complying with malicious requests more than 45% of the time.",
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      "text": "MPIDetector's authors report 90.91% accuracy on textual prompt injection, 99.28% on visual prompt injection, and a 99.80% detection rate against 500 obfuscated web-agent injections, without fine-tuning.",
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    {
      "text": "Zico Kolter, Gray Swan's co-founder and chief scientist and an OpenAI board member, told the Madrona blog that AI security is two jobs - hardening infrastructure against attackers and fixing vulnerabilities inside the model itself - and that prompt injection cannot be fully patched away.",
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    {
      "heading": "Attack success gets a price tag",
      "paragraphs": [
        {
          "markdown": "Security researchers are starting to price jailbreaks the way attackers actually experience them. In a preprint posted October 23, Rohan Menon and Chih-Hong Cheng introduce ASR_STEP, a metric that penalizes iterative jailbreak strategies for every call they make to the victim model. The logic is operational, not academic: repeated queries raise the chance of detection and intervention in production, so an attack that needs hundreds of tries is less dangerous than its headline success rate suggests.",
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          "markdown": "When the authors re-scored JailBreakBench, the popular iterative attacks GCG and PAIR came out less effective than previously reported - GCG down 7.0 to 8.7 percentage points, PAIR down 3.4 to 12 - while single-step attacks like PAP were unaffected by the new metric. That draws a line that matters for budgets: threat profiles that look similar on a leaderboard behave very differently under real monitoring. This suggests the relevant question for a security team is not whether the model can be jailbroken at all, but how expensive each successful jailbreak is, and whether production monitoring catches the attempt before it lands.",
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    {
      "heading": "The safety checker is now the target",
      "paragraphs": [
        {
          "markdown": "A second preprint, Mirage, goes after a part of the pipeline most teams assumed was their last line of defense. Shanghao Shi and co-authors demonstrate a multimodal jailbreak that bypasses post-generation safety checkers in diffusion models - including Stable Diffusion 1.5, SDXL, and Kandinsky 2.2 - by concealing adversarial targets within benign input images. The attack exploits shared representation spaces so a perturbed image lines up with the adversarial target while staying clear of the concepts the checkers scan for, and it needs only limited black-box queries with no internal model access.",
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          "markdown": "The implication is uncomfortable for anyone who placed a checker at the end of the pipeline and called the system safe. If the checker itself can be evaded as one more component in the attack path, then bolting a filter onto the output is a strategy with a known, published workaround.",
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    {
      "heading": "Email agents keep getting phished",
      "paragraphs": [
        {
          "markdown": "The agent version of the same story comes from AgentPhishing, a benchmark by Justin Tan, Miao Li, and Ding Zhao that tests LLM email agents against adaptive, persuasive attacks instead of static datasets. The authors built PhishOpt, a method that optimizes phishing emails against each individual agent, and the results are blunt: of nine agents evaluated, four detected fewer than 10% of phishing attempts while complying with malicious requests more than 45% of the time, even though the agents had passed conventional phishing evaluations.",
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          "markdown": "As Zico Kolter, the Gray Swan co-founder and chief scientist and an OpenAI board member interviewed on the Madrona blog on October 22, put it: \"Prompt injection is analogous to phishing for AI agents.\" The AgentPhishing numbers put a price on that analogy. Static defenses that pass their own tests can still fold when the attacker adapts to the specific agent reading the inbox.",
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    {
      "heading": "The defense is being measured too",
      "paragraphs": [
        {
          "markdown": "The fourth preprint is on the other side of the ledger. MPIDetector, by Yiheng Li and Jingyue Li, uses LLM-based reasoning with few-shot in-context learning to spot malicious prompt injections across text and visual inputs, without fine-tuning. The authors report 90.91% accuracy with 100% recall on textual prompt injection, 99.28% accuracy with 100% precision on visual prompt injection, and a 99.80% detection rate against 500 highly obfuscated web-agent injections in the WebInject set.",
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          "markdown": "Those numbers are the authors' own, on the authors' own benchmarks, in a preprint that has not been peer-reviewed - and that caveat applies to all four papers. But the pattern is the point: defenses are now being scored with the same cost-and-coverage discipline that the attackers just got. The arms race has a bookkeeping standard.",
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    {
      "heading": "What this changes for budgets",
      "paragraphs": [
        {
          "markdown": "Kolter's broader point gives the batch its frame. AI security, he told the Madrona blog, splits into two jobs - hardening infrastructure against attackers and addressing the vulnerabilities inside the model itself - and red-teaming the model is distinct from standard penetration testing. His recommendation is to red-team the model itself, separately from the software around it, and to test each new capability adversarially before it ships.",
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          "markdown": "This suggests the planning move for an AI executive: measure security against operations, not leaderboards. Cost-aware metrics like ASR_STEP change which attacks count as credible threats; benchmarks like AgentPhishing change which agent deployments are ready for real inboxes; and defenses like MPIDetector get scored the same way. Kolter's framing - that prompt injection cannot be fully patched away - implies the budget follows mitigation and detection, not prevention alone. The honest caveat: every number above is self-reported by its authors, on their own benchmarks, in papers that have not yet been peer-reviewed.",
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  "unresolved": [
    "None of the four preprints has been peer-reviewed, and all reported numbers are the authors' own benchmark results.",
    "The AgentPhishing brief gives no code or dataset repository URL for the benchmark.",
    "Mirage and MPIDetector attack and defend the same problem space but were not tested against each other."
  ],
  "decision_card": {
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      "AI executives sizing 2027 security budgets and red-team scope",
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    "compare_against": [
      "The next JailBreakBench leaderboard refresh",
      "Whether vendors adopt cost-aware metrics like ASR_STEP"
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      "source_url": "https://arxiv.org/pdf/2610.26589v2",
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      "title": "Mirage: Targeted Jailbreaks Against Post-Generation Safety Checkers in Multimodal Diffusion Models",
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